N IST SP 1 2 3 0 - 3

Community Resilience-Focused Technical Investigation of the2016 LUMBERTON, NORTH CAROLINA FLOOD
COMMUNITY IMPACT AND RECOVERYFOLLOWING SUCCESSIVE FLOOD EVENTSEDITORS
Jennifer Helgeson
Sara Hamideh
Elaina J. SutleyThis publication is available free of charge from:https://doi.org/10.6028/NIST.SP.1230-3

NIST Special Publication 1230-3

The Lumberton, North Carolina Flood of
2016, Wave 3: A Community Impact andRecovery-Focused Technical
Investigation Following Successive Flood
Events
Editors
Jennifer Helgeson
National Institute of Standards and Technology
Sara Hamideh
Stony Brook University
Elaina J. Sutley
University of Kansas
This publication is available free of charge from:https://doi.org/10.6028/NIST.SP.1230-3
October 2021

U.S. Department of Commerce
Gina M. Raimondo, Secretary
National Institute of Standards and Technology
James K. Olthoff, Performing the Non-Exclusive Functions and Duties of the Under Secretary ofCommerce for Standards and Technology & Director, National Institute of Standards and Technology

Disclaimer No. 1
Certain commercial entities, equipment, or materials may be identified in this document in orderto describe an experimental procedure or concept adequately. Such identification is not intended
to imply recommendation or endorsement by the National Institute of Standards and Technology,
nor is it intended to imply that the entities, materials, or equipment are necessarily the best
available for the purpose.
Disclaimer No. 2
The information contained herein is provided as a public service with the understanding that
Colorado State University makes no warranties, either expressed or implied, concerning theaccuracy, completeness, reliability, or suitability of the information. Nor does Colorado State
University warrant that the use of this information is free of any claims of copyright infringement.
Disclaimer No. 3
In compliance with the Paperwork Reduction Act, NIST personnel did not participate in thecollection or analysis of household survey data. NIST personnel did participate in the field
inspections of damaged residential housing and collected data on the event and subsequent
response by public officials and private sector entities.
Disclaimer No. 4
All maps in the report, except where noted, were created using ESRI ArcGIS.

ii

Author Credits and Acknowledgements
Each section within each chapter of this report was prepared and edited by a number of different
authors from the field study team and leadership. In addition, the entire study team is grateful to
all those in and around Lumberton and within the State of North Carolina who shared theirknowledge and perspective through interviews and meetings with our researchers. The field study
team acknowledges the generous contributions to this study and associated reports.
Chapter 1: Elaina Sutley, Sara Hamideh, Jennifer Helgeson, Maria Dillard, John van de Lindt,
Walter Gillis Peacock, and Judith Mitrani-Reiser
Chapter 2 and associated Appendices Authorship: Shane Crawford, Elaina Sutley, Trung Do,
Omar Nofal, Tori Johnson, Maria Watson, Jonathan Weigand
Chapter 3 and associated Appendices Authorship: Sara Hamideh, Elaina Sutley, Jarrod Loerzel,
Nathanael Rosenheim, Donghwan Gu
Chapter 4 and associated Appendices Authorship: Jennifer Helgeson, Maria Watson, Juan F. Fung
Chapter 5 Authorship: Elaina Sutley, Jennifer Helgeson, Sara Hamideh

Funding Acknowledgement
This research was conducted as part of the NIST Center of Excellence for Risk-Based Community
Resilience Planning under Cooperative Agreement 70NANB15H044 (Wave 3 site visit) and
70NANB20H008 (Data processing/reporting) between the National Institute of Standards andTechnology (NIST) and Colorado State University. The content expressed in this report are theviews of the authors and do not necessarily represent the opinions or views of NIST or the U.S
Department of Commerce.
Suggested Citation
Helgeson, J., Hamideh, S., and Sutley, E.J. (Eds.). (2021). Community Resilience-Focused
Technical Investigation of the 2016 Lumberton, NC Flood: Community Impact and Recovery
Following Successive Flood Events. NIST Special Publication 1230-3.
Field Study Team Members
The Lumberton Wave 3 Field Study Team consisted of 20 people. As described in this report,
Wave 3 consisted of three trips to Lumberton. There were four major teams and many people
participated on more than one team; these included the Damage Assessment Team, Housing Team,the Business Team, and the Public Sector Team. Team affiliations are listed as of the time of Wave
3 (April 2019).

iii

Field Study Leadership
Elaina J. Sutley, University of Kansas
Sara Hamideh, Stony Brook University
Jennifer F. Helgeson, National Institute of Standards and Technology
Field Study Team Members
Wave 3aCrawford, Shane, National Institute of Standards and Technology
Do, Trung, University of Louisiana Lafayette
Graettinger, Andrew, University of Alabama
Johnson, Victoria, U.S. Naval Academy
Nofal, Omar, Colorado State University
Sutley, Elaina, University of Kansas
Watson, Maria, Texas A&M University
Weigand, Jonathan, National Institute of Standards and Technology
Wave 3b
Hamideh, Sara, Stony Brook University
Khajehei, Sayma, University of Utah
Wave 3c
Crawford, Shane, National Institute of Standards and Technology
Dillard, Maria, National Institute of Standards and Technology
Do, Trung, University of Louisiana Lafayette
Fung, Juan, National Institute of Standards and Technology
Greathouse, Allison, Iowa State University
Gu, Donghwan, Texas A&M University
Hamideh, Sara, Stony Brook University
Harrison, Kenneth, National Institute of Standards and Technology
Helgeson, Jennifer, National Institute of Standards and Technology
Khajehei, Sayma, University of Utah
Loerzel, Jarrod, National Institute of Standards and Technology
Rosenheim, Nathanael, Texas A&M University
Sabarethinam, Kameshwar, Louisiana State University
Seoul, Kijin, Texas A&M University
Sutley, Elaina, University of Kansas
Tobin, Jennifer, University of Colorado Boulder
van de Lindt, John, Colorado State University
Watson, Maria, Texas A&M University
Other Contributors
Mitrani-Reiser, Judith, National Institute of Standards and Technology
Peacock, Walter, Texas A&M University
Van Zandt, Shannon, Texas A&M University
Xiao, Yu, Portland State Universityiv

Glossary
ACS
AEO
ARC
CDBG-DR
CRP
CSV
DFS
DOI
DS
EDGe$
EL
FEMA
FFE
F&F
GIS
HF
HM
HMGP
HWM
HUD
HVAC
IAA
IHP
IN-CORE
IRB
LiDAR
LP
NAICS
NCST
NIST
NGO
NOAA
OR
PPS
PRA
SBA
SFD
TraCR
USGS

American Community Survey
Applied Economics Office
Alternatives for Resilient Communities
Community Development Block Grant- Disaster Recovery
Community Resilience Program
Comma-Separated Values
Disaster and Failure Studies
Digital Object Identifier
Damage State
Economic Decision Guide Software
Engineering Laboratory
Federal Emergency Management Agency
First Floor Elevation
Friends and Family
Geographic Information Systems
Hurricane Florence
Hurricane Matthew
Hazard Mitigation Grant ProgramHigh Water Marks
Department of Housing and Urban Development
Heating, Ventilation, and Air Conditioning
Interagency Agreement
Individuals and Households Program
Interdependent Networked Community Resilience
Modeling
Institutional Review Board
Light Detection and Ranging
Liquified Petroleum
North American Industry Classification System
National Construction Safety TeamNational Institute of Standards and Technology
Nongovernmental Organization
National Oceanic and Atmospheric Administration
Operations Research
Probability Proportion to Size
Paperwork Reduction Act
Small Business Administration
Single Family Dwelling
Tracking Community Resilience
United States Geological SurveyvExecutive Summary
In early October 2016 Hurricane Matthew crossed North Carolina as a category 1 hurricane withsome areas receiving 15 in. to 18 in. (38 cm to 46 cm) of rainfall on already saturated soil. The
NIST-funded Center for Risk-Based Community Resilience Planning (Center) teamed withresearchers from NIST’s Community Resilience Program, Disaster and Failure Studies Program,and the Applied Economics Office to conduct a quick response field study focused on the smallcity of Lumberton, NC and the flooding experienced from the Lumber River. Approximately one
year later, the Center and NIST team returned to Lumberton to document and better understandLumberton’s recovery progress with an emphasis on housing, businesses, schools, community andstate-level decisions, and the intersection of these sectors in community recovery. The longitudinal
study of Lumberton continued, with the inclusion of a damage investigation after Lumberton wasaffected again in September 2018 by Hurricane Florence. The Hurricane Florence impact andrecovery investigation was completed approximately six months later.
Longitudinal investigations are critical for the study of community resilience so that impact,decision, and recovery data are comparable across time and inferences may be made on the effects
of decisions and impacts from one period on those in future periods. These data and analyses arereported here to support guidance and recommendations on what is needed to improve
communities’ ability to recover quickly and equitably, and what attributes make communities more
resilient to natural hazards in general.
This third in a series of community resilience-focused field studies is presented herein as Wave 3of the on-going Lumberton, North Carolina Flood of 2016 Report Series. Recall that Wave 1
documented the initial physical and socio-economic impacts of the flooding on the community,
particularly for housing, households, schools, and other parts of the public sector. Wave 2 began
the assessment of recovery from Hurricane Matthew with continued focus on housing, households,
schools, parts of the public sector; a new focus area on business impacts and recovery was added.
In the midst of recovering from Hurricane Matthew, the Lumberton community was flooded again
by Hurricane Florence in 2018. Thus, the Wave 3 field study was conducted across three periods,
taking place in September 2018, December 2018, and April 2019. There were three major
objectives in Wave 3 data collection and analysis: first, to document the initial damage caused by
Hurricane Florence to the longitudinal sample and community of Lumberton; second, to document
the point in time progress of Lumberton’s recovery from both Hurricanes Matthew and Florence;
third, to understand individual and collective capacity gained from the experience with Hurricane
Matthew that helped many to be better prepared for Hurricane Florence. Wave 3 data collection
dealt primarily with the impact and recovery process of the most heavily affected housing andbusinesses through an initial damage investigation and two systematic surveys, as well asinterviews and meetings with selected public officials. Wave 3 additionally included a special trip
to interview public housing residents and city-level decision-makers at a critical point when theCity was making rebuilding decisions on the damaged public housing; this portion of Wave 3,
termed Wave 3b, is not covered in this report. Analysis revealed that much of Lumberton had
physically repaired structural damage from Hurricane Matthew at the time of Hurricane Florence,but housing recovery for some households and economic recovery was on-going and compoundedby the second extreme flood event in two years. Recovery from both events is likely to be ongoing for years to come. Also, the Wave 3 surveys revealed increased individual mitigation andvipreparedness measures employed, including but not limited to more evacuations, physical changes
that reduced vulnerability of houses and businesses, more preparation and awareness measures,and faster cleanup immediately after Hurricane Florence.
In Wave 3, the team returned to the same housing units surveyed in Waves 1 and 2, and businesses
from Wave 2 to first assess physical damage caused by Hurricane Florence in September 2018,
and then in April 2019 to continue to assess continued recovery from Hurricane Matthew, learn
about disruption and recovery from Hurricane Florence, and document capacity-enhancing
measures. The Wave 3a damage surveys revealed approximately two-thirds of the sampled
housing units that were damaged after Hurricane Florence experienced more severe damage after
Hurricane Matthew. In total, approximately 18% of sampled housing units and 15% of sampledbusinesses were damaged by Hurricane Florence. The fact that significant proportions of oursampled housing and businesses were not damaged was due to the nature of the sample design,where many buildings were outside of the Hurricane Florence inundation area, the differences inthe two flood events, and noted differences in the city’s preparation for Hurricane Florencecompared to Hurricane Matthew. The Wave 3a team observed different mitigation actions that had
had been taken at the city-level, as well as by individuals. At the city-level, a system of three berms
was built at the I-95/CSX underpass in the days leading up to Hurricane Florence. This represented
a more concerted effort to mitigate flooding than what had been done leading up to Hurricane
Matthew in 2016, and was likely due to lessons learned from the Hurricane Matthew experience.
Additionally, the Lumberton Water Plant and an electrical power network substation weresurrounded by temporary berms to keep flood waters out. Hydraulic pumps were placed over the
I-95/CSX berm system, east and south of the levee. Collectively, these mitigation actions likely
improved outcomes from the flooding experience after Hurricane Florence, they also influenced
the measurement science approaches adopted by the field study team. Situational flags wereincorporated into the data collection smartphone application to record whether it appeared housing
units and businesses had been reoccupied since Hurricane Matthew, and whether the highwatermark was more likely a relic of Hurricane Matthew than caused by Hurricane Florence. The fieldstudy team also noticed households and businesses took individual-level preparedness actions, andresponded differently, more resiliently, to Hurricane Florence as compared to Hurricane Matthew.
These actions included elevating contents inside the buildings off of the floor and into upper stories
or high shelves, elevating exterior air conditioning condenser units off of the ground ontopedestals, relocating duct systems from crawlspaces to attics, and evacuating. While individuallevel mitigation measures were only captured anecdotally in Wave 3a, they informed survey designfor Wave 3c. In the surveys conducted during Wave 3c there was systematic collection of data
about mitigation to understand if and how widespread these actions were and to understand if andhow they may have reduced property loss and collective damage.
The Wave 3c housing surveys documented that 33 % of households who completed the survey
reported still having unrepaired damage from Hurricane Matthew at the time of Hurricane
Florence. Because of Hurricane Florence, nearly two-thirds of respondents were dislocated fromtheir home for at least one day, where the majority of households who responded to the survey
returned home within two weeks. Homeowners and occupants of single-family dwellings weredislocated for longer periods, on average, compared to renters and occupants of multi-familydwellings. Although recovery was still an active process, 85 % of respondents reported intentionsof remaining in their home for the next year, and more than 80 % indicated having the same accessvii

to school and grocery stores after Hurricane Florence compared to before Hurricane Matthew.
Similar to observations in Wave 2, which was conducted one year after Hurricane Matthew, in
Wave 3c small proportions of respondents received insurance payouts and other recovery
resources. The Wave 3c survey asked about positive impacts of the two events as well, whereapproximately one-third of respondents indicated increased community involvement andapproximately 40 % indicated increased contact with neighbors and extended family since before
Hurricane Matthew. Both changes have positive impacts towards human capacity and social
capital, and thereby community-level resilience. Many households made a disaster plan with theirhousehold members before Hurricane Florence. After Hurricane Florence, 40 % more households
elevated their hot water heater and/or HVAC, 20 % more households elevated interior contents toprotect them from flood waters, 32 % had their home assessed by a structural engineer, and 60 %re-routed ductwork from below the home’s floor and into the attic space. Longitudinalcomparisons are provided throughout Chapter 3. Across Waves 1, 2, 3a, and 3c, our data show anincreasing number of abandoned homes in Lumberton since Hurricane Matthew.
The Wave 3c business survey data documented that 17 % of businesses that completed the survey
reported unrepaired damage from Hurricane Matthew at the time of Hurricane Florence. Due toHurricane Florence impacts, 80 % of businesses reported losing electricity for at least one day,which was the most frequently reported utility loss. Furthermore, over 40 % of businesses
experienced interrupted operations for at least one week. When asked to report their perception of
their recovery, over half of businesses (58 %) reported being fully recovered relative to their stateprior to Hurricane Florence. Additionally, the majority of businesses (78 %) reported no change
in revenue as a result of Hurricane Florence. The Wave 3c survey asked business representatives
to indicate mitigation and preparedness actions they have taken. Some of these are relevant only
to those who own their business facility (i.e., floodproofing of building, secured a secondary
storage location, structural assessment of building). Fewer businesses reported taking these actions
compared to housing with the most frequent reported as flood proofing building (25 %), themajority of whom did so between before Hurricane Matthew (10 %) or Hurricane Matthew and
Hurricane Florence (14 %). The business survey also asked about mitigation, adaptation, andpreparedness measures relevant to all respondents (renters and owners of facilities). There had
been much greater uptake of these actions since the Hurricanes, e.g., 48 % of businesses reported
adopting strategies to stay informed of weather warnings and 47 % have an emergency plan.
Longitudinal comparisons are provided throughout Chapter 4. Across Waves 2, 3a, and 3c our data
show the fragility of recovery. After Hurricane Matthew (Wave 2), most respondents reported
being mostly recovered. Immediately before Hurricane Florence, most respondents reported thestatus of their business as between mostly recovered and fully recovered; however, after Florence,
this drops back down to mostly recovered (Wave 3c).
Overall, the field team concludes that two years after Hurricane Matthew, and six months after
Hurricane Florence flooded Lumberton, recovery was underway with a long road of continued
recovery ahead for the community of Lumberton. This Wave 3 report summarizes the longitudinal
study design and the results obtained through the damage investigation immediately after
Hurricane Florence, and the housing and business surveys. Collectively, these findings
quantitatively and qualitatively document the impacts and compounded recovery process for thecommunity of Lumberton, NC. The report provides conclusions from all data collection efforts,
and next steps for the longitudinal study.

viii

TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION ............................................................................................. 1
1.1 The NIST Center of Excellence for Risk-Based Community Resilience Planning.............. 1
1.2 The Engineering Laboratory at NIST ................................................................................... 2
1.3 Overview of Hurricane Matthew and Wave 1 (2016) and Wave 2 (2018) Field Studies .... 4
1.4 Overview of the Wave 3 (2018-2019) Lumberton Field Study ............................................ 7
CHAPTER 2 PHYSICAL DAMAGE OBSERVATIONS FOLLOWING HURRICANEFLORENCE .............................................................................................................................. 9
2.1 Goals and Objectives ............................................................................................................ 9
2.2 Comparing the 2016 and 2018 Lumberton Floods ............................................................... 9
2.3 Sampling ............................................................................................................................. 13
2.4 Survey Instrument ............................................................................................................... 14
2.5 Data Collection Methodology............................................................................................. 18
2.6 Survey Results .................................................................................................................... 19
2.7 Observations of the City’s Collective Preparation and Response ...................................... 22
2.7.1 Berm System Built at I-95/CSX Underpass ................................................................ 23
2.7.2 Flood Mitigation for Critical Infrastructure Systems .................................................. 27
2.7.3 Hydraulic Pumps ......................................................................................................... 28
2.8 Observations of Households and Businesses: Individual-Level Preparation andResponse ................................................................................................................................... 29
CHAPTER 3 HOUSING DISRUPTION AND RECOVERY ............................................ 31
3.1 Goals and Objectives .......................................................................................................... 31
3.2 Sampling Procedure ............................................................................................................ 31
3.3 Survey Instrument ............................................................................................................... 32
3.3.1 Establishing Occupancy and Study Eligibility ............................................................ 33
3.3.2 State of Repair following Hurricane Matthew............................................................. 33
3.3.3 Initial Damage, Utility Outage, and Repair Progress following Hurricane Florence .. 34
3.3.4 Household Dislocation after Hurricane Florence ........................................................ 34
3.3.5 Housing Recovery In-Place after Hurricanes Matthew and Florence ......................... 34
3.3.6 Educational Recovery .................................................................................................. 35
3.3.7 Preparedness, Mitigation, and Social Capital .............................................................. 35
3.3.8 Household Socio-Demographic Characteristics .......................................................... 36
3.4 Data Collection Methodology............................................................................................. 36ix

3.4.1 Daily Operations .......................................................................................................... 36
3.4.2 Data Management ........................................................................................................ 37
3.4.3 Data Cleaning Process ................................................................................................. 38
3.5 Housing Survey Results ...................................................................................................... 40
3.5.1 Survey Response Rates ................................................................................................ 40
3.5.2 Findings: Hurricane Florence Flood Impacts and Recovery ....................................... 41
CHAPTER 4 BUSINESS INTERRUPTION AND RECOVERY ...................................... 60
4.1 Goals and Objectives .......................................................................................................... 60
4.2 Sampling Procedure ............................................................................................................ 60
4.3 Survey Instrument ............................................................................................................... 63
4.4 Data Collection Methodology............................................................................................. 65
4.4.1. Field Procedures ........................................................................................................ 65
4.4.2. Data Entry and Cleaning ............................................................................................ 66
4.5 Results................................................................................................................................. 67
4.5.1 Hurricane Matthew Recovery Follow-up ................................................................... 67
4.5.2 Hurricane Florence Damage and Interruption ............................................................ 70
4.5.3 Hurricane Florence Recovery ..................................................................................... 74
4.5.4 Business Information and Respondent Details ........................................................... 77
4.5.5 Longitudinal Trends.................................................................................................... 79
CHAPTER 5 CONCLUSIONS AND NEXT STEPS .......................................................... 86
5.1 Conclusions for Damage Measurement following Successive Flood Events .................... 86
5.2 Conclusions for Housing Disruption and Recovery ........................................................... 87
5.3 Conclusions for Business Interruption and Recovery ........................................................ 88
5.4 Next Steps for the Lumberton Longitudinal Field Study ................................................... 89
REFERENCES ....................................................................................................................... 91
APPENDIX 2A ..................................................................................................................... A-1
APPENDIX 2B ..................................................................................................................... A-4
APPENDIX 2C ..................................................................................................................... A-8
APPENDIX 2D ..................................................................................................................... A-9
APPENDIX 3A ................................................................................................................... A-10
APPENDIX 3B ................................................................................................................... A-15
APPENDIX 3C ................................................................................................................... A-16
APPENDIX 4A ................................................................................................................... A-17x

APPENDIX 4B ................................................................................................................... A-25
APPENDIX 4C ................................................................................................................... A-26

xi

LIST OF TABLES
Table 2-1. Flood-based damage state descriptions for residential buildings(van de Lindt et al. 2018) .............................................................................................................. 16
Table 2-2. Flood-based damage state descriptions for commercial buildings(Xiao et al., 2020) .......................................................................................................................... 17
Table 3-1. Housing survey response rates ..................................................................................... 41
Table 3-2. Hurricane Matthew-induced damage to home ............................................................. 42
Table 3-3. Hurricane Florence-induced damage to home ............................................................. 42
Table 3-4. Hurricane Florence-induced utility outage and duration by tenancy ........................... 44
Table 3-5. Hurricane Florence-induced cellular and landline phone outage and duration bytenancy ........................................................................................................................................... 45
Table 3-6. Household dislocation and duration by housing type following
Hurricane Florence ........................................................................................................................ 47
Table 3-7. External influences on decision to return home after Hurricane Florence................... 48
Table 3-8. Change in household access to essential needs after Hurricane Matthew ................... 50
Table 3-9. Insurance coverage, insurance payment receipt, and timing after
Hurricane Florence ........................................................................................................................ 51
Table 3-10. Household recovery resource inquiries ...................................................................... 52
Table 3-11. Resources received and timing of receipt after Hurricane Matthew .......................... 52
Table 3-12. Resources received and timing of receipt after Hurricane Florence .......................... 53
Table 3-13. Covering repair and replacement costs ...................................................................... 53
Table 3-14. Mitigation and preparedness strategies taken by households before Hurricane
Florence and at the time of the wave 3c survey (after Hurricane Florence) ................................. 54
Table 3-15. Change in community involvement and contact with neighbors and extended family
since Hurricane Matthew ............................................................................................................... 55
Table 3-16. Race and ethnicity of surveyed households ............................................................... 56
Table 3-17. Maximum household member education level of surveyed households ................... 56
Table 3-18. Annual household income of surveyed households ................................................... 57
Table 4-1. Sector distribution of sample ....................................................................................... 62
Table 4-2. Operating status of the business sample ...................................................................... 67
Table 4-3. Recovery status from Hurricane Matthew immediately before Hurricane Florence... 68
Table 4-4. Self-reported financing sources used for recovery from the impacts of Hurricane
Matthew (n=49) ............................................................................................................................. 68
Table 4-5a. Businesses that still required repairs from Hurricane Matthew impacts immediately
before Hurricane Florence ............................................................................................................. 69xii

Table 4-5b. Percent of remaining repairs still need to be completed for those that still require
some sepairs from Hurricane Matthew pre-Hurricane Florence ................................................... 69
Table 4-6. Extent to which Hurricane Florence affected ongoing business recovery from
Hurricane Matthew ........................................................................................................................ 70
Table 4-7. Damage states reported by the businesses resulting from Hurricane Florence ............ 70
Table 4-8. Percent of businesses reporting utilityl and the number of days of disruption
resulting from Hurricane Florence ................................................................................................ 71
Table 4-9. Self-reported issues for employees trying to get to the business following
Hurricane Florence ........................................................................................................................ 72
Table 4-10. Importance of decisions of surrounding businesses (those on the same block) to thedecision for the business to remain open or to close ..................................................................... 74
Table 4-11. Business profitability change as a result of Hurricane Florence ................................ 74
Table 4-12. Business self-reported recovery status after Hurricane Florence ............................... 75
Table 4-13. Insurance coverage, claim receipt, and timing after Hurricane Florence .................. 76
Table 4-14. Assistance requested and received by businesses ...................................................... 77
Table 4-15. Businesses’ self-reported federal cassifications ......................................................... 78
Table 4-16. Businesses’ self-reported ownership structure ahead of Hurricane Florence ............ 78
Table 4-17. Race and ethnicity of surveyed business owners/managers....................................... 79
Table 4-18. Mitigation, adaptation, and preparedness measures for respondents that own thebusiness premises/storefront .......................................................................................................... 80
Table 4-19. Mitigation, adaptation, and preparedness measures for all respondents .................... 81xiii

LIST OF FIGURES
Figure 1-1. Path and intensity timeline of Hurricane Matthew ....................................................... 5
Figure 1-2. Path and intensity timeline of Hurricane Florence ....................................................... 7
Figure 1-3. Lumberton Field Study timeline ................................................................................... 8
Figure 2-1. Map of Lumberton showing digitized differences in flood extents using NOAA aerial
imagery, where solid orange polygons indicate areas flooded by Hurricane Matthew (HM) but
not Hurricane Florence (HF), and dashed green polygons indicate areas flooded by Hurricane
Florence (HF) but not Hurricane Matthew (HM). Dashed black rectangles indicate regions in the
study area where the most difference in flooding occurred........................................................... 10
Figure 2-1a. Residential neighborhood located North of the Lumber River and West ofInterstate 95 indicated by (a) in Figure 2-1. Imagery collected following Hurricane Matthew(NOAA 2016) and Hurricane Florence (NOAA 2018) has been edited to show flooded areas
more clearly ................................................................................................................................... 11
Figure 2-1b. Mixed commercial and residential area South of the Lumber River bound by thelevee and railroad indicated by (b) in Figure 2-1. Imagery collected following Hurricane
Matthew (NOAA 2016) and Hurricane Florence (NOAA 2018) has been edited to show flooded
areas more clearly .......................................................................................................................... 12
Figure 2-1c. Residential area South of the Lumber River and railroad indicated by (c) in Figure2-1. Imagery collected following Hurricane Matthew (NOAA 2016) and Hurricane Florence(NOAA 2018) has been edited to show flooded areas more clearly ............................................. 13
Figure 2-2. Figure 2-2. SFD in Wave 3a, total sample size includes 567 housing units in thelongitudinal sample, 46 housing units added in Wave 3a, and 229 businesses in the longitudinalsample ............................................................................................................................................ 14
Figure 2-3. Distribution of overall damage to residential and commercial buildings due toHurricane Florence ........................................................................................................................ 20
Figure 2-4. Distribution of damage to interior contents of residential and commercial buildings
due to Hurricane Florence ............................................................................................................. 21
Figure 2-5. Spatial distribution of difference in damage states assessed for residential buildings
after Hurricanes Matthew and Florence ........................................................................................ 22
Figure 2-6. I-95/CSX Underpass seen in 2016: (a) from above; (b) from underneath, with flood
prevention set in place. (NOAA 2016). Photograph shown in (b) reproduced with permissions
from van de Lindt et al., 2018 ....................................................................................................... 23
Figure 2-7. Berm system deployed before Hurricane Florence: (a) Aerial overview with threeberm sections marked (NOAA 2018); (b) Aerial overview with image locations and orientations(NOAA 2018); (c) Primary berm section; (d) LiDAR scan of primary berm section; (e)Secondary berm section; (f) LiDAR scan of secondary berm section; (g) Tertiary berm section;(h) LiDAR scan of tertiary berm section ....................................................................................... 25
Figure 2-8. Digital elevation model (USGS 2017) showing elevations in area of Lumberton
South of the levee, with CSX railroad and levee locations noted ................................................. 27

xivFigure 2-9. Lumberton water plant and an Electrical power network ubstation in the leveed areaof Lumberton that flooded in: (a) 2016 from Hurricane Matthew (NOAA 2016) with (b) Closeup of Lumberton Water Plant, and (c) 2018 from Hurricane Florence (NOAA 2018) with (d)Close-up of the electrical substation.............................................................................................. 28
Figure 2-10. Examples of visible individual-level mitigation efforts in Lumberton implemented
before the 2018 Flood; (a) Temporary perimeter flood protection around international retailerbuilding (NOAA 2018); (b) Elevated air conditioning condensers in a public
housing complex ........................................................................................................................... 30
Figure 3-1. Number of days to repair home .................................................................................. 43
Figure 3-2. Utility service outage time .......................................................................................... 46
Figure 3-3. Dislocation time after Hurricane Florence.................................................................. 47
Figure 3-4. Number of days household member missed work since the beginning of 2018 due tohousing issues caused by flooding ................................................................................................ 49
Fig. 3-5. Sankey diagram depicting perceived or confirmed occupancy and abandonment of
housing sample across Waves 1, 2, 3a, and 3c .............................................................................. 59
Fig. 3-6. Venn diagram with four sets depicting the number of housing units consistently
recorded as abandoned in one or more waves ............................................................................... 59
Figure 4-1. Number of days until businesses to resumed operations after Hurricane Florence (inseven-day increments) ................................................................................................................... 73
Figure 4-2. Return of business capacity after Hurricane Florence (in 10 % increments), reported
at the time of Wave 3c data collection .......................................................................................... 73
Figure 4-3. Business operational capacity: after Matthew, before Florence, and after
Florence ......................................................................................................................................... 82
Figure 4-4. Average number of employees reported: before Matthew, between Matthew andFlorence, and after Florence .......................................................................................................... 83
Figure 4-5. Recovery status after Matthew, between Matthew and Florence, and after
Florence ......................................................................................................................................... 84
Figure 4-6. Business profitability: before Matthew, between Matthew and Florence, and after
Florence ......................................................................................................................................... 85

xv

Chapter 1: Introduction
The longitudinal study of Lumberton, North Carolina described in this report is a collaboration
between researchers from the National Institute of Standards and Technology (NIST)-funded
Center of Excellence for Risk-Based Community Resilience Planning (Center), and researchers in
the Engineering Laboratory at NIST.

1.1 The NIST Center of Excellence for Risk-Based Community Resilience
Planning
Community resilience depends on the performance of the built environment and social, economic,
and public institutions which, individually and collectively, are essential for immediate response
and for long-term recovery of communities following a disaster. Collective community needs andobjectives, including post-disaster recovery goals, are not reflected in codes, standards, and otherregulatory documents applied to the design of individual facilities. This necessitates an approach
which reflects the complex interdependencies among the physical, social, and economic systems
on which a healthy community depends. Thus, modeling the resilience of communities against thedisruption caused by natural hazards and disasters depends on many disciplines, including
engineering, social sciences, and information sciences. In the wake of climate change, it is
becoming more likely and more common for communities to be faced with a major disaster before
being able to fully recover from the previous disaster. Providing detailed and community-specificguidance on how to better prepare for and recover from disasters is the impetus for the Center’s
research. The Center, headquartered at Colorado State University in Fort Collins, Colorado andinvolving eleven additional universities at the time of Wave 3, was established by NIST in 2015.
The Center’s overarching goal is to establish the measurement science for community resilienceassessment and risk-informed decision-making. To accomplish this goal, the Center is engaged inthree major research thrusts aimed at: (1) developing a community resilience modelingenvironment – the “Interdependent Networked Community Resilience Modeling Environment” or
IN-CORE – to quantitatively assess alternative community resilience strategies, (2) developing astandardized data ontology, robust architecture, and management tools to support IN-CORE, and(3) performing a comprehensive set of disaster hindcasts to validate IN-CORE’s advanced
modeling environment. A longitudinal field study is planned and executed approximately every
12 months in the same location with the same sample of housing units and businesses to support
the following phases of resilience model development within IN-CORE: impact, disruption,
dislocation, recovery, decision, and interdependency. The Lumberton, NC field study will provide
comprehensive data sets to evaluate the information needed for validation of the full architecture.
The Center works to accelerate the development of system-level models and databases that will
provide the technology for enhancing community resilience. Team members, at the time of Wave
3, include noted resilience experts from the University of Colorado-Boulder, the University ofOklahoma, Oregon State University, Rice University, Texas A&M University, the University of
Illinois, the University of Washington, the University of South Alabama, California Polytechnic
University-Pomona, the University of Kansas, and Iowa State University. Ultimately, the decisionframework created by the Center will provide decision-makers with a unique set of tools that canbe tailored to the needs of individual communities. These tools will optimize the design andsubsequent management of individual facilities and interdependent infrastructure systems toachieve resilience goals while managing life-cycle costs. Its use will provide a basis for targeting
public investments and incentives for private investments, thus making it possible to establish a“business case” for achieving community resilience.

1.2 The Engineering Laboratory at NISTThe Engineering Laboratory (EL) at NIST promotes U.S. innovation and industrial
competitiveness by advancing measurement science, standards, and technology for engineered
systems in ways that enhance economic security and improve quality of life. Some of the ways the
EL carries out its mission is by undertaking activities in community resilience, disasters andbuilding failure investigations, economic analysis and life cycle assessment, wind and seismic
hazard impact reduction, fire prevention and control, engineering, and manufacturing materials.
Several researchers from the Applied Economics Office (AEO) and the Community Resilience
Program(CRP) from the EL participated in the Lumberton field study to advance the disastermetrology research of the EL’s Disaster and Failure Studies Program as well as to advance themeasurement and modeling work needed to support community resilience planning.
Hazard events stress buildings and infrastructure in ways and on a scale that cannot be easily
replicated in a laboratory – buildings and infrastructure are built without being tested at full scale.
The study of disaster and failure events is essential to improving the performance of buildings andinfrastructure, the safety of building occupants, and associated evacuation and emergency response
procedures. NIST leads a multi-disciplinary Disaster and Failure Studies (DFS) Program within
the EL intended to standardize disaster field deployment, assessment, and reporting protocols toultimately improve building and infrastructure performance. This program implements these goals
through the following means: (1) monitoring events using a screening tool to evaluate whether
decision criteria merits the establishment and deployment of a study team, (2) coordinating theestablishment, deployment, operations and reporting of study teams, (3) ensuring that the study
team’s safety, health and environmental requirements are met including relevant hazard reviews,
training, and personal protective equipment prior to deployment, (4) building and maintaining
effective partnerships and communications with other federal agencies, state/local governments,
stakeholders and the general public, (5) establishing and executing standard operating procedures
and criteria for disaster and failure studies, (6) promoting the implementation of recommendations
from all DFS investigations, (7) creating and maintaining an archival data repository for DFS, (8)carrying out the statutory requirements of the National Construction Safety Team (NCST) Act,
which includes providing the Secretariat for the NCST Advisory Committee and annual reports toCongress, and (9) overseeing a disaster metrology research program that interacts with othergroups in EL, to directly inform best practices for means (1) to (7).
NIST manages a multi-faceted program, assisting communities and stakeholders on issues related
to buildings, the interdependencies of physical infrastructure systems, and the social andeconomics functions they support. The Community Resilience Program, part of NIST's broader
disaster resilience work, complements efforts by others in the public and private sectors.
Community Resilience Program focuses on research, community planning and guidance, andstakeholder engagement. In addition to improvements to standardized field study protocols, thethree following projects within the Community Resilience Program are directly supported by thefield study in Lumberton, NC.

2

Development of a Community-Resilience Systems Model – The main challenge confronting design
or planning for community resilience is the need to address the large scale of the resilience system
in question and the need to address social, political, budgetary and other constraints. In thedevelopment of community plans (e.g., land use management, emergency response, economic
development), the formidable challenges inherent in both the analysis and design of the resiliencesystems must be addressed. The analyses must account for or include: a) stochastic phenomena(e.g., timing and severity of hazards, component failure); b) dependencies between and among theresilience subsystems; c) time aspects of resilience (e.g., from preparedness through long-term
recovery, aging infrastructure); d) connection of the built environment to the social and economic
services it supports; e) significant uncertainties in knowledge; f) metrics to quantify community
resilience, and; g) the large amount and diversity of data needed to characterize the resilience of acommunity’s systems.
In this project, a model is being developed to support community resilience decision-making. Thismodel will be accessible to planners and others with a need to examine alternative options forresilience. The NIST Alternatives for Resilient Communities (NIST ARC) model and its methods
will first be tested in a research version of the model (NIST ARC-R). Data collected during fieldstudies for past hazard events will be used to both inform and validate NIST ARC. The results of
the NIST ARC model are being systematically compared with IN-CORE simulation models toensure both models can provide accurate results for their intended users.
To address these significant challenges, the research plan adopts an operations research (OR)approach to the problem of community resilience planning. Operations research is a quantitative
approach that is uniquely suited to large scale problems. A main tool of operations research ismathematical programming. Formulations (or “math programs”) that describe community
resilience problems are developed and then are solved using available optimization solvers. Theinteractive formulation and solution of these models effectively provides decision-makers theability to identify cost-effective, resilience-improving alternatives that can be the basis of thesolutions ultimately adopted.
Development of a Community Resilience Assessment Methodology –NIST released the Community
Resilience Planning Guide for Buildings and Infrastructure Systems to help communities plan andimplement prioritized measures for the built environment to strengthen their resilience to hazard
events. The next step of the project is to provide communities with the tools necessary to evaluate
and measure their resilience over time. A more resilient community will have, among many othercharacteristics, improved functionality of buildings and infrastructure systems and a shorterrecovery time of community functions following disruption.
This project is developing the methodology required to build a community-scale resilienceassessment tool. The methodology is based on a foundational understanding that community
functions are linked to buildings and infrastructure systems. Examples of community functions are
the following: housing/shelter, the economy, health, education, sustenance, public safety,
communication, transportation, religion/culture, and recreation/entertainment. Each function isdelivered through interconnected components of the social-economic system (e.g., the banking
system, health care system, personnel/staff, consumers) and the physical system (e.g., building
clusters, transportation networks, communication networks). Both social and physical systems

3

influence community resilience – or a community’s ability to function after a disruptive hazard
event.
The final methodology and accompanying Tracking Community Resilience (TraCR) tool will
include the following: selected priority indicators, the analytical approach(es) for computing eachindicator over time in a relevant manner for at least one spatial scale, best practices for how theapproach can be replicated for different spatial scales, public data sources for all indicators, datavisualization for the indicators, multivariate analyses to examine relationships between indicators,
sensitivity and uncertainty analysis, and validation studies.
Developing Cost-Effective Resource Allocation Strategies to Enhance Community Resilience Advancements in measurement science are needed to estimate the economic impact associatedwith community resilience planning for natural and human-made hazards. Currently, disasterrelated loss (damage) estimates are available, although they tend to focus on direct loss only andare at aggregate levels. These estimates often fail to consider down-stream, indirect, and sustained
effects, such as business interruption, which can be large and have a significant effect on the shortand long-term stability of a local or regional economy. Also missing are estimates of the cost of
prevention, protection, and mitigation of hazards and potential disaster events, as well as theexpenditures required during the response and recovery phases. A methodology to value theeconomic impact and avoided costs is needed to evaluate the return-on-investment of community
resource allocation decisions made to reduce future economic damages from disturbances anddisasters, while recognizing the need to achieve balance with other community goals, and toaccount for uncertainty.
This project includes five major activities: (1) the creation and formal acceptance of draft standardpractices to formalize, publish, and make available an economic toolkit facilitating decision
making for communities; (2) measurement of disturbance and disaster-related costs and potential
linkages to achieve other community goals; (3) measurement of disaster losses, focusing on majorindirect losses, such as business interruption, and distributional effects —through the use of bothdata gathered in the field through surveys and interviews as well as secondary data sources; (4)quantification of the uncertainty affecting economic decisions, and understanding how uncertainty
should be communicated to decision makers; and (5) measure the ‘resilience dividend,’ the (nondisaster related) community co-benefits from investing in disaster resilience, and provide tractableguidance to communities on approaches to assessing the net co-benefits associated with resilienceplanning.
To date, this project has produced the Community Resilience Economic Decision Guide for
Buildings and Infrastructure Systems, which can be used as a standalone product or in combination
with the Community Resilience Planning Guide for Buildings and Infrastructure Systems. The
process has also been developed into an online application titled the Economic Decision Guide
Software (EDGe$) Online Tool (Helgeson et al., 2020).

1.3 Overview of Hurricane Matthew and Wave 1 (2016) and Wave 2 (2018)
Field Studies
In early October 2016, after devastating parts of the Caribbean, Hurricane Matthew struck Florida,
and continued up the eastern seaboard before turning east into the Atlantic Ocean off the coast of4

North Carolina and Virginia (see Figure 1-1). More than 170 counties in Florida, Georgia, South
Carolina, and North Carolina were included in Presidential Emergency Declarations and/or
Presidential Disaster Declarations between October 6th and 11th, 2016. Economic loss estimates
due to Hurricane Matthew exceed $10 billion (NOAA, 2018).
More than a week after the storm turned out to ocean, parts of North Carolina had yet to experience
flood crests, with many communities experiencing flood levels at or higher than those for
Hurricane Floyd in 19991. The
Lumber River reached flood stage in
Lumberton, North Carolina on
October 3rd due to local heavy rains.
On October 11th, the Lumber River
crested at almost 22 ft. (6.7 m) above
the gage datum. The water level
slowly fell, dropping below floodlevel on October 23, 2016.
As indicated in van de Lindt, Peacock,
Mitrani-Reiser et al. (2018), theCenter Field Study team selected
Lumberton for longitudinal study formany reasons, including the moderate
population size of approximately 21000 residents (US Census Bureau2017), the diverse socio-demographic
makeup of primarily three race andethnicity groups (White, Black, andLumbee Indian), and that flood waters
entered the City through a gap in thelevee system that was reported to notmeet the current FEMA regulations
13 years prior.
Figure 1-1. Path and intensity timeline of Hurricane Matthew.

The NIST-funded Center for RiskBased
Community
Resilience
Planning teamed with researchers from NIST’s Engineering Laboratory, specifically theCommunity Resilience, Disaster Failure Studies, and Applied Economics programs, to conduct aquick response field study focused on the city of Lumberton, NC and the flooding Lumberton
experienced from the Lumber River. The quick response field study was performed during theweek of November 29, 2016. Denoted here as Wave 1, it was the first of a series of annual fieldstudies to document and better understand Lumberton’s recovery. Data collection during Wave 1
focused on the residential housing sector with two primary objectives: to establish and document
1

At its peak, Hurricane Floyd was recorded as a Category 4 hurricane. It reduced to a Category 2 by early
September 1999 when it impacted North Carolina with a storm surge height exceeding 9 feet causing 51 fatalities
and billions in damages. Flooding damage was tremendous with as much as 20 feet of flood water staying for over a
week in some areas and exacerbated due to Hurricane Dennis which hit North Carolina just a few weeks prior.

5

initial conditions for the longitudinal field study of Lumberton’s recovery, with a focus on themost heavily affected area located within a particular school zone; and, to facilitate and document
the development and first application of a combined engineering-social science field study
protocol that provides a quantitative linkage between flood damage and socio-economics including
race, ethnicity, income, tenure status, and education level. Population dislocation probabilities
were found to be higher for Black and Native American households than for White households,
despite the presence of the same residential housing damage states following the flood. See van deLindt, Peacock, Mitrani-Reiser, et al. (2018) for the Wave 1 field investigation report.
Approximately one year after Hurricane Matthew, during the dates of January 19th to 29th, 2018,
the Center and NIST researchers returned to Lumberton for Wave 2 of the longitudinal study. As
indicated in Sutley, Dillard, van de Lindt et al. (2021), the overall purpose of Wave 2 was to (1)support on-going research in the Center and NIST through the collection of the necessary data tobuild and/or validate community-resilience models for business, housing, social institutions, andbuilding functionality; and (2) advance understanding on the factors that influence recovery fortwo specific community sectors, namely housing and business, as well as to gain information on
the recovery status of schools, households, public works, and the community as a whole.
In support of the overall purpose of the longitudinal study, the same housing sample from Wave 1
was adopted for Wave 2, and as a new feature, a sample of 453 businesses was added. For housing,the Wave 1 sample units deemed invalid were dropped, resulting in a sample of 567 housing units
for structured surveys in Wave 2. Of these 567 housing units, the team was able to gain survey
responses from more than 227 household during wave 3c. For businesses, a random stratified
sampling approach resulted in a sample of 350 businesses drawn from the ReferenceUSA database(InfoGroup, 2016). While in the field, an additional sample of 103 businesses was drawn to addresspossible coding errors in ReferenceUSA, business closures, and response rates. The final sample
included 453 businesses, where 164 business owners and managers responded to the surveys.
Although Wave 1 data collection did not include damage assessments for this sample of businesses(as they were not a component of the study sample at that time), the business survey instrumentused in Wave 2 assessed the damage from Hurricane Matthew through a series of questions built
on analogous damage state descriptions designed for commercial buildings. Both the housing andbusiness surveys included questions on physical repair and sector-specific recovery indicators to
document recovery progress and asked about the availability and timing of a range of financial
recovery resources. For the public sector data collection, meetings were held with four cityrepresentatives and four state representatives, including both government and the water utility, tounderstand the context for recovery of the community. Likewise, to understand the context forschool recovery, interviews were conducted with nine school district representatives. At the timeof Wave 2, recovery was still on-going for Lumberton households and businesses, with much of
the allocated Federal relief and mitigation funds not yet arrived, and many rebuilding decisions
yet to be made. See Sutley, Dillard, van de Lindt, et al. (2021) for the Wave 2 field investigation
report.

6

1.4 Overview of the Wave 3 (20182019) Lumberton Field Study
Hurricane Matthew was a 500-year rainfall
event. Only two years later Hurricane
Florence, a 1000-year rainfall event,inundated Lumberton along with manyother areas in North Carolina. Thoughprobabilistically such events may occur insuccession, especially insight of climatechange conditions that increase thefrequency and intensity of such events(IPCC, 2021), this combination of
hurricanes creates a challenge to
Lumberton as it strives to recovery. Afterreaching peak intensity and Category 4
status on September 11, 2020, Hurricane
Figure 1-2. Path and intensity timeline of Hurricane
Florence made landfall on the U.S. Eastern
Florence.
Coast on September 14, 2020 as aweakened Category 1 hurricane (see Figure1-2), bringing six days of heavy rainfall to North and South Carolina. The storm eventually loststrength over West Virginia and was downgraded to a post-tropical cyclone on September 17,
2018. Hurricane Florence inundated the city of Lumberton with 6.5 inches (165 mm), 15 inches(380 mm), and 13.8 inches (350 mm) of rain on September 15, 16, and 17 of September 2018,
respectively.
Due to its slow forward motion and heavy rains, the storm caused significant coastal and inland
flooding. In North Carolina, 22 stream gages measured record peak flood stages due to the storm,with many breaking records previously set by Hurricane Matthew (Stewart and Berg, 2019). PostHurricane Florence flooding significantly affected businesses, housing, and agriculture in many
areas that were still recovering from the 2016 flooding.
The Center and NIST team conducted a quick response field study from October 16 to 19, 2018 to
document the initial physical damage to the longitudinal sample of housing units and businesses,
denoted as Wave 3a. As critical decisions were being made by the City on whether and how torebuild and repair damaged public housing units since Hurricane Matthew, a small team of Center
researchers returned to Lumberton December 2 to 5, 2018, to conduct focused interviews with key
decision makers and public housing stakeholders, denoted as Wave 3b. From April 11 to 21, 2019,
the Center and NIST team returned to Lumberton to execute two systematic surveys – one onhouseholds and one on businesses – and conduct semi-structured interviews with school
administrators and City officials to learn more about the impact and disruption caused by
Hurricane Florence, the progress or hindrance on recovery still unfolding after Hurricane Matthew,
and the cascading challenges created by these two subsequent disasters for the community. Figure
1-3 provides the timeline of Lumberton field studies executed by the Center and NIST team to
date.

7

Figure 1-3. Lumberton Field Study timeline.
This report is organized as follows: Chapter 2 focuses on the damage investigation immediately
following Hurricane Florence. Chapter 3 provides the housing disruption and recovery from thesuccessive flood events, including specific study goals, team training and deployment, and results.
In Chapter 4, the business survey results on disruption and recovery from successive flood eventsis provided in a similar fashion as Chapter 3. Finally, Chapter 5 provides conclusions on theprevious chapters’ findings, and next steps for the longitudinal field study.

8Chapter 2: Physical Damage Observations Following Hurricane Florence
2.1 Goals and Objectives
This chapter presents Wave 3a, including goals, planning and coordination, data collection, andfindings. Following the occurrence of Hurricane Florence, it was imperative to assess the initialphysical damage caused to the longitudinal study’s housing and business samples to document andunderstand these impacts on the on-going recovery from Hurricane Matthew. The Wave 3adamage assessment provided a rare opportunity to observe the confluence of damage from two
extreme flood events that occurred in close temporal proximity in a single community, and tocontrast the community- and individual-scale impacts arising from each event. The details on the
Wave 3a initial damage assessment measurement and findings to housing and business samples
following Hurricane Florence are provided in this chapter. Damage assessment included
documenting high-water marks (HWMs) using digital surveys and photographic imagery,assessing damage levels to the building exterior and, when possible, the building interior, including
contents, inventory, machinery, and equipment. Although not systematically collected,
community-level preparations and individual-level flood mitigation efforts appeared to haveresulted in a measurable reduction in the impacts of the flood. Wave 3a aimed to understand themitigation actions taken at the individual and city levels to be followed up on systematically insubsequent trips, starting with Wave 3c.

2.2 Comparing the 2016 and 2018 Lumberton Floods
Hurricane Florence in 2018 resulted in the second major flood event (following Hurricane Mathew
in 2016) experienced by Lumberton in less than a three-year period. Upon the formation andpredicted trajectory of Hurricane Florence affecting North Carolina, virtual reconnaissance wasimmediately implemented by our team. Once impact occurred, virtual reconnaissance documented
the flood extent and damage caused by Hurricane Florence in Lumberton through monitoringsocial media and online updates from local news sources, United States Geological Survey (USGS)
stream gage data, and National Oceanographic and Atmospheric Administration (NOAA) aerial
imagery and reporting updates. This initial information was used to compare the flood extent anddamage differences between Hurricane Florence and Hurricane Matthew through Geographic
Information System (GIS) data analysis. Additionally, the virtual reconnaissance findings
provided direction for the Wave 3a data collection and field activities.
Figure 2-1 provides digitized regions of flood coverage showing differences between the two
events based on aerial imagery captured by NOAA (NOAA, 2016; 2018). In Figure 2-1, inset
photographs provide aerial imagery in these regions for each event for comparison, where the inset
map in Figure 2-1(a) depicts the area of flooding within the Lumberton city limits. USGS
hydrograph data were used to verify that the aerial images were captured on the dates of peakflooding for the two events. Solid orange polygons designate regions that were flooded after
Hurricane Matthew (HM) but not after Hurricane Florence (HF); dashed green polygons designate
regions that were not flooded after Hurricane Matthew but were flooded after Hurricane Florence.
From Figure 2-1, it is apparent that the area south of the Lumber River was more severely flooded
by Hurricane Matthew than in Hurricane Florence, while areas north of the river, particularly aresidential neighborhood west of I-95 (highlighted in top-right inset), were more severely flooded
by Hurricane Florence than by Hurricane Matthew. Mitigation actions taken by the City of9Lumberton in preparation for the flooding from Hurricane Florence likely contributed to thechanges in the flood extents. These mitigation actions included building a temporary berm at the
gap in the levee system where flood water entered the region south of the river during Hurricane
Matthew (for more information on the levee system and gap, refer to van de Lindt et al., 2018;2020), and using hydraulic pumps to quickly move water from the leveed area into the downstream
river channel. The measurement science approaches taken by our team in Wave 3a (discussed in
the next sections) accounted for individual- and community-level mitigation efforts, as they likely
reduced damages from the flooding experienced in 2018.

Figure 2-1. Map of Lumberton showing digitized differences in flood extents using NOAA aerial
imagery, where solid orange polygons indicate areas flooded by Hurricane Matthew (HM) but
not Hurricane Florence (HF), and dashed green polygons indicate areas flooded by Hurricane
Florence (HF) but not Hurricane Matthew (HM). Dashed black rectangles indicate regions in the
study area where the most difference in flooding occurred.
10Figure 2-1a. Residential neighborhood located North of the Lumber River and west ofInterstate 95 indicated by (a) in Figure 2-1. Imagery collected following Hurricane Matthew(NOAA 2016) and Hurricane Florence (NOAA 2018) has been edited to show flooded areas
more clearly.

11

Figure 2-1b. Mixed commercial and residential area South of the Lumber River bound by thelevee and railroad indicated by (b) in Figure 2-1. Imagery collected following Hurricane
Matthew (NOAA 2016) and Hurricane Florence (NOAA 2018) has been edited to show flooded
areas more clearly.

12Figure 2-1c. Residential area South of the Lumber River and railroad indicated by (c) in Figure2-1. Imagery collected following Hurricane Matthew (NOAA 2016) and Hurricane Florence(NOAA 2018) has been edited to show flooded areas more clearly.

2.3 Sampling
Wave 3a investigated physical damage to the same housing and business samples as Waves 1 and
2 with addition of a small number of housing units that were impacted by Hurricane Florence but
not previously impacted by Hurricane Matthew. The virtual reconnaissance comparison of aerial
imagery identified 46 single-family dwellings (SFD) that met this criterion; all 46 were
investigated in Wave 3a. A map of the business and housing samples is provided in Figure 2-2,
where blue markers indicate the longitudinal housing sample, red markers indicate the longitudinalbusiness sample, and orange markers indicate the 46 additional SFDs investigated in Wave 3a.

13

Figure 2-2. SFD in Wave 3a, total sample size includes 567 housing units in the longitudinalsample, 46 housing units added in Wave 3a, and 229 businesses in the longitudinal sample.

2.4 Survey Instrument
The Wave 3a damage survey, provided in Appendix 2A, was very similar to the damage survey
developed and utilized in Wave 1 (Deniz et al., 2019). Changes made to the damage survey acrosswaves (1) were intended to account for damage specific to commercial buildings, sincecommercial buildings were not part of Wave 1, (2) improved how the flood information was
documented, including measuring the HWM and first floor elevation, based on lessons learned
from Wave 1, and (3) included format changes that accommodated the use of a smart phone
application, Survey123 for ArcGIS©, as opposed to using paper forms. Damage data and floodinundation measurements were collected using Survey123. This approach was advantageouscompared to using paper surveys because it allowed researchers to (i) ensure that all required fields
were populated before a survey was submitted, (ii) limit the amount of time required for datatransfer, (iii) link all collected photographs to the specific building for which they were taken, and(iv) enable near real-time analysis and visualization of the survey results for remote researchers to
14

allow quality assurance to take place while team members were in the field. Institutional Review
Board (IRB) approval was not required for Wave 3a because data collection did not involve humansubjects; however, as was true in past waves, instrument testing and team training were critical,
particularly given the virtual format of the instrument.
The survey and data collection methodology used in Wave 3a was similar to that in the Wave 1
deployment and followed guidance from the United States Geological Survey (Koenig et al.,2016). The Wave 3a deployment included measurements of HWM with respect to ground and FFEwith respect to ground and retained ground and FFE datums for measurements of flood height. In
addition to measurements, the survey form included documentation of building typology (singlefamily, multi-family, or mobile home for residential structures, and attached or detached building
for commercial structures) as well as foundation type.
Also similar to Wave 1, physical damage was classified into five discrete, sequential damage states
differentiated for residential and commercial buildings (see Appendix 2B for the full damage state
criteria used in Wave 3a). Damage scales were similar for both occupancy categories, but with theinspection criteria tailored to common structural building types, nonstructural finishes, and interior
contents. Discrete damage states for residential buildings were classified by observed damage tothe foundation, exterior walls, and interior contents, including the floors, electrical and plumbing
systems, drywall, and kitchen cabinets (see Table 1-1). More details on how these damage states
were formulated are available in van de Lindt et al. 2018; 2020.

15

Table 2-1. Flood-based damage state descriptions for residential buildings(van de Lindt et al., 2018).
DamageState

Description0

No damage: water may enter crawlspace or touch foundation (crawlspace or slab
on grade) but water has no contact to electrical or plumbing, etc. in crawlspace,and no or limited contact with floor joists. No sewer backup into living area.

1

Minor water enters house; damage to carpets, pads, baseboards, flooring.
Approximately 25.4 mm (1.00 in), but no drywall damage. Touches joists. Could
have some mold on subfloor above crawlspace. Could have minor sewer backup
and/or minor mold issues.

2

Drywall damage up to approximately 0.61 m (2.0 ft) and electrical damage, heaterand furnace and other major equipment on floor damaged. Lower bathroom andkitchen cabinets damaged. Doors or windows need replacement. Could have major
sewer backup and/or major mold issues.

3

Substantial drywall damage, electrical panel destroyed, bathroom/kitchen cabinets
and appliances damaged; lighting fixtures on walls destroyed; ceiling lighting may
be ok. Studs reusable; some may be damaged. Could have major sewer backup
and/or major mold issues.

4

Significant structural damage present; all drywall, appliances, cabinets etc.
destroyed. Could be floated off foundation. Building must be demolished or
potentially replaced.

The discrete damage states for commercial buildings are provided in Table 2. These damage states
were originally developed and used in Wave 2, albeit through a different mode of damage
assessment (Sutley et al., 2021). Exterior damage to commercial buildings was minor tononexistent given that these building exterior materials are not typically damaged by floodinundation. However, the interior of commercial buildings was assessed, including the flooring
and baseboards, electrical and plumbing systems, drywall, equipment, and doors and windows.
Minor or major sewer backup, and degree of mold growth were also assessed.

16

Table 2-2. Flood-based damage state descriptions for commercial buildings(Xiao et al., 2020).
DamageState

Description0

No damage: No contact to electrical or plumbing, etc. in crawlspace. No contact
with floor joists. No sewer backup

1

Water touches floor joists up to minor water enters building; damage to carpets,
pads, baseboards, flooring. Approximately 25.4 mm (1.00 in) in the building but
no drywall damage. Could have some mold in crawlspace. Could have minor
sewer backup and/or minor mold issues.

2

Water level approximately 0.61 m (2.0 ft) with associated drywall damage andelectrical damage, water heater and other major equipment. Doors or windows mayneed replacement. Could have major sewer backup and /or major mold issues.

3

Water level 0.61 m to 2.44 m (2.0 ft to 8.0 ft); substantial drywall damage,electrical panel destroyed, office cabinets or storage racks; lighting fixtures onwalls destroyed; ceiling lighting may be ok. Studs reusable; some may bedamaged. Could have major sewer backup and/or major mold issues.

4

Significant structural damage present; all drywall, cabinets etc. destroyed. Could
be floated off foundation. Building must be demolished or potentially replaced.

The Wave 3a survey also recorded whether interior contents had been removed from the residenceor commercial building, where imagery comparisons from past data collection waves wererequired to determine whether interior content removal was done before or after Hurricane
Florence. Proper evaluation of the degree of interior damage present in a previously flooded
building was challenging and often required entry-access to the interior of the structure todifferentiate new from unrepaired damage. Entering damaged structures was not always possible.
Removal of interior contents was generally visible from the street, either by debris piled near theroad or debris removal marks in lawns or on pavement. In selected instances, missing or replacedinterior contents were directly observable through doors or windows.
In addition to the quantitative evidence of physical damage, evidence of occupancy was recorded
for each sample. Evidence of occupancy was determined based on the surveyor’s assessment using
specific business- and residence-specific indicators, similar to what has been done in previous
waves of data collection in Lumberton. Businesses were labeled occupied if they were at least
partially open to the public. Alternatively, if the business was not open at the time of the survey,
but had normal business hours posted for times outside of the times when the team was collecting
data, it was accordingly marked as evidence of occupancy. For residences, if direct evidence ofoccupancy was observed including the household residents being present, verification from aneighbor that the household was occupied, or presence of an undamaged and well-maintained
vehicle in the driveway, then the surveyor marked evidence of occupancy accordingly. In addition,
if the surveyor was able to determine that the interior had not been damaged, then the household

17was marked as occupied unless there were real-estate signs or other indicators that no one wasliving there at that time.

2.5 Data Collection Methodology
All eight members of the Wave 3a team arrived in Lumberton on Tuesday, October 16, 2018. Upon
arrival, the team conducted a cursory vehicle-based inspection of several of the neighborhoods
where damage in Hurricane Florence was apparent in aerial imagery. The team then underwent
preparations to begin data collection the following day, including reviewing the data collection
methodology, and safety training. Before beginning damage surveys, the field inspection teamconvened a training session to (i) confirm team members were familiar with Survey123 and thesurvey instrument, (ii) answer any remaining questions about damage state interpretation, (iii)
provide guidance on standard measurement strategies, such as measuring HWM and FFE
elevations from the lowest ground elevation around each sample building, and (iv) recount bestpractices based on the experiences of field study members of the Wave 1 deployment. In addition
to the training session, on the first field day the team collectively filled out a survey form for aresidential structure in the sample. The structure was in an area that was not flooded from
Hurricane Florence, but the drywall, flooring, and other finishes were removed from the building
interior and a distinct HWM was visible. This led to the creation of ‘situational flags,’ which wererecorded in a comment field in the Survey123 form. Compounding impacts from the two flooding
events, which did not necessarily flood the same areas or at the same level of inundation, required
this situational flagging in a comment field included in the damage survey to indicate damage thatwas still unrepaired or HWMs remaining from Hurricane Matthew. The team created one flag toindicate that a structure had not been reoccupied since Hurricane Matthew, and a second flag todesignate that the HWM was more-likely a relic of Hurricane Matthew than caused by Hurricane
Florence.
The Wave 3a damage survey commenced on the morning of Wednesday, October 17, 2018, andlasted through midday on Friday, October 19, 2018. An individual electronic survey was initiatedfor each building in the sample, and then filled out and closed before leaving the property. The
results of the Wave 3a damage survey found a confluence of flood damage resulting from both
Hurricanes Matthew and Florence, and limited wind damage from Hurricanes Florence and as wellas Hurricane Michael, which made landfall in Florida on October 10, 2018 and moved inland
creating limited wind damage in Lumberton.
Each day, the broader team of eight investigators was subdivided into teams of two. The teamtraveled by vehicle, with two teams per vehicle, to a selected area and proceeded on-foot to visit
and survey sample buildings in the area before moving to a new area. A GoogleTM map was created
and loaded with the sample locations to navigate while in the field. All eight investigators
reconvened at midday and in the evening to upload any locally-stored (on the investigators’smartphones) damage survey results via reliable network access or WIFI hotspot. The map was
updated to exclude those properties already surveyed each time the team convened; this helped
ensure efficiency in surveying. Daily meetings were held in the evenings between the investigators
and a support team of remote researchers tracking the field activities to communicate preliminary
observations, develop or revise planning for the subsequent day, and perform quality assurance
checks of data collected that day.

18

In addition to building information, and documenting damage states, photographs were taken in
Survey123 to document observed damage and HWM measurements. The use of Survey123 forphotographs was particularly useful as each photograph was automatically associated with thedamage survey for the building represented in the photograph.
While the decision to refrain from conducting structured data collection that required institutional
review board approval was made to expedite field deployment, this changed the approach tomeasurement of damage as compared to that of Wave 1. In Wave 1, residents often verified thatthe HWMs measured by the deployment team represented the maximum height of inundation or,
in some cases, informed team members of higher water marks that were not visible from the frontof the home. Additionally, residents often invited team members into their homes during Wave 1
which permitted team members to classify interior damage and, in some cases, photograph interior
damage. This was not as common in Wave 3a. In situations where team members interacted
directly with residents, information sheets describing the Lumberton Longitudinal Study, theCenter, and NIST (see Appendix 2C) were provided, as well as information about mental healthand financial resources (see Appendix 2D).
In addition to the damage inspections performed for residential and commercial buildings in
Lumberton, an assessment of the I-95/CSX underpass and berm system was conducted tounderstand the impact of the community-level preparation for the event on the damages observedby the field team. The assessment included collecting LiDAR laser scans of the remains of theberm system to provide measurements of berm height and preserve the perishable data.

2.6 Survey Results
After returning from the field, data cleaning measures were conducted to ensure quality andconsistency of the captured data. The findings of the data analysis are presented below.
As previously discussed, at the time of Wave 3a, there remained buildings that had not been fully
repaired from damage incurred by flooding following Hurricane Matthew. This was often
evidenced by a gutted interior or determined by speaking with a neighbor, and when suspected, aflag was created in the data collection. While the detailed extent of damage or repair that existed
immediately before Hurricane Florence is unknown, using a combination of information including
Wave 2 repair state, visible high-water marks, sparse input from neighbors or occupants, andindicators of recently removed interior items, damage states were assessed for Hurricane Florence
flood damage alone. Figure 2-3 shows the spatial distribution of damage caused by Hurricane
Florence to our sample of residential and commercial buildings. A histogram of the damage
distribution is included in the top-left corner of the figure. Of the housing units inspected, 409
were assessed as DS 0, 44 were assessed as DS 1, 41 were assessed as DS 2, 1 was assessed as DS
3, and 2 were assessed as DS 4. Of the businesses inspected, 117 were assessed as DS 0, 6 wereassessed as DS 1, 9 were assessed as DS 2, 6 were assessed as DS 3, and 0 were assessed as DS 4.

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Figure 2-3. Distribution of overall damage to residential and commercial
buildings due to Hurricane Florence.
Figure 2-4 provides the overall interior damage states for commercial and residential buildings
with a histogram provided in the top left corner. Interior damages were assessed for 124
commercial buildings and 362 housing units and interiors were not assessable for 149 buildings.
Of the residential buildings in which interiors were assessed, 22 were assessed as DS 1, 23 wereassessed as DS 3, and 3 were assessed as DS 4. Of the commercial buildings in which interiors
were assessed, 3 were assessed DS 1, 10 were assessed DS 2, and 2 were assessed DS 3.

20

Figure 2-4. Distribution of damage to interior contents of residential and commercial buildings
due to Hurricane Florence.
Finally, the difference in damage states recorded between Hurricanes Matthew and Florence isshown in Figure 2-5. Figure 2-5 includes only those 316 residential buildings that were assessedin both Wave 1 and Wave 3a and does not include commercial buildings because businesses were
not part of the scope in Wave 1. In calculating the differences in Figure 2-5, Hurricane Florence
damage states were subtracted from Hurricane Matthew damage states; therefore, a positive
number in the histogram indicates a higher damage state in Hurricane Matthew than Hurricane
Florence. Approximately two-thirds (66 %) of residential buildings (n=209) sustained moredamage after Hurricane Matthew than after Hurricane Florence, while only 3 % of residentialbuildings (n=8) sustained more damage after Hurricane Florence compared to after Hurricane
Matthew. Almost one-third (31 %) of the residential buildings (n=99) had the same degree ofdamage after both events; however, of those 99 buildings, 72 % were not flooded by either
Hurricane. The high proportion of undamaged buildings is due in part to the nature of the sample
created for the Wave 1 damage assessment, which deliberately included non-flooded buildings tobetter understand the socio-economic factors leading to flood vulnerability and the dependencies
within a community. The spatial distribution of positive and negative values in Figure 2-5 can be21

used to interpret factors influencing the observed differences in damage for individual residentialbuildings more closely.

Figure 2-5. Spatial distribution of difference in damage states assessed for residential buildings
after Hurricanes Matthew and Florence.

2.7 Observations of the City’s Collective Preparation and Response
The City of Lumberton employed multiple mitigation strategies in the days before Hurricane
Florence made landfall in North Carolina to reduce the severity and impact of flooding. Thesemitigation strategies included (i) building a system of temporary berms at the CSX/I-95 underpass,(ii) building a temporary berm around a water treatment plant and setting up flood barriers aroundan electrical substation, and (iii) deploying hydraulic pumps inside the leveed area to moveaccumulated flood water back into the stream system. Each of these are discussed in the following
subsections.

222.7.1 Berm System Built at I-95/CSX Underpass
To provide context for the flood mitigation taken at the I-95/CSX underpass leading up to the flood
generated by Hurricane Florence, Figure 2-6 shows the actions taken by the city at the underpass
leading up to the 2016 flood in an effort to mitigate its impacts. Figure 2-6(a) shows an aerial viewof the underpass taken at the time of highest inundation during Hurricane Matthew, with thelocations of the CSX Railroad and VFW Road labeled. Figure 2-6(b) shows the use of backfill andsandbags in the ditches between the overpass abutments, railroad and road to create a constantelevation across the gap in the levee. The flood water exceeded the elevation of the underpass andsurged into the city, causing damage to VFW road and undercutting the CSX railroad at thelocation of the dotted circle in Figure 2-6(a). For more information on the mitigation, damage, andrepairs to the underpass made in 2016, refer to van de Lindt et al. (2018; 2020).

Figure 2-6. I-95/CSX Underpass seen in 2016: (a) from above; (b) from underneath, with flood
prevention set in place. (NOAA, 2016). Photograph shown in (b) reproduced with permissions
from van de Lindt et al., 2018.

23

A system of three temporary berms was built at the I-95/CSX underpass in the days leading up to
Hurricane Florence, which represented a more concerted effort to mitigate flooding than that takenin the days leading up to Hurricane Matthew in 2016. The increased mitigation activity leading up
to Hurricane Florence was likely due to the recent experience with flooding from Hurricane
Matthew. The last flood event before Hurricane Matthew had occurred over ten years previously
and did not include flood water entering the leveed area of the city through the I-95/CSX
underpass. Figure 2-7 provides the locations of the berms, designated in Figure 2-7(a) by a dotted
red, a dashed yellow, and a solid green line. Figure 2-7(b) shows the locations and orientations of
the images shown in Figure 2-7(c)-(h). A berm section, considered here to be the primary line ofdefense protecting water from entering the city, was built between the underpass abutments and isdenoted by a red dotted line in Figure 2-7(a). This berm was built by placing highway dividers,
backfilling sand on the east side of the dividers, and adding sandbags on the west (river) side ofthe dividers, as shown in Figure 2-7(c). This berm breached on the southwest side, allowing flood
waters to enter the city, similar to what occurred in 2016. Following the flood, the material fromthe temporary berm structure overlaying the railway was removed to allow train access; therefore,
measurement of the berm height in that region and assessment of the extent of the breach were notpossible. It is assumed that the height of the berm in the region, that was gone at the time of
measurement, was level with the remaining portion of the berm.
Figure 2-7(c) depicts the northeast section of the berm built atop VFW Road, which was remained
during the Wave 3a deployment. Another berm was built along the southwest side of the railroad
and is denoted by a yellow dashed line in Figure 2-7(a). This berm, considered here to be thesecondary line of defense due to its protection of the railroad, was constructed by placing highway
dividers along the railroad and adding sand, asphalt riprap, and sandbags on the southwest side ofthe dividers, as shown in Figure 2-7(e). Parts of the berm were destroyed and the constituent
material was carried away by the flood. A third berm is denoted by a solid green line in Figure 27(a). This berm was built out from the east side of the bridge abutment toe slope, crossing VFW
Road, running east along the south side of VFW road, crossing the railroad and connecting to theberm section running along the railroad. This third berm, considered a tertiary line of defense as it
protected the area north of the railroad, was constructed by placing highway dividers andbackfilling the eastern side of the dividers with sand and asphalt riprap, as shown in Figure 2-7(g).
This berm was still intact while the Wave 3a team was in the field, and aerial imagery shows thatthe flood water was directed south of the railroad by the location of the breach of the primary bermand by the secondary berm.

24Figure 2-7. Berm system deployed before Hurricane Florence: (a) Aerial overview with threeberm sections marked (NOAA 2018); (b) Aerial overview with image locations andorientations (NOAA 2018); (c) Primary berm section; (d) LiDAR scan of primary berm
section; (e) Secondary berm section; (f) LiDAR scan of secondary berm section; (g) Tertiary
berm section; (h) LiDAR scan of tertiary berm section.

25

A series of six Light Detection and Ranging (LiDAR) scans were administered on 17 October
2018 at various locations around the underpass to collect survey-grade measurements of theremnants of the berms. LiDAR scans produce a dense, three-dimensional point cloud with anaccuracy of ±1 cm. The scans were used to measure heights of the berms with respect to VFW
Road and CSX Railroad elevations. Figure 2-7(d) shows the point cloud representing the primaryberm built between I-95 overpass abutments, where the CSX railroad is visible in the left side of
the figure. The height of the berm was measured to be 1.43 m (4.69 ft) above the crest of VFW
Road. Figure 2-7(f) shows the point cloud representing the secondary berm built along the railroad.
The height of the berm measured 1.11 m (3.64 ft) above the elevation of the railroad. The crest of
VFW Road measured 0.16 m (0.52 ft) above the railroad. Figure 2-7(h) shows the point cloud
representing the tertiary berm built from the east abutment toe slope (i.e., baseline section) andconnecting to the secondary berm running along the railroad. The height of the berm measured
1.44 m (4.72 ft) above the crest of VFW Road.
The primary berm stretching between abutments was breached at its edges. The southwestern edge
suffered a major breach, allowing the flood water to surge through, which is apparent in Figure 27(a). The southeast edge of the berm was partially breached and allowed only a minor amount ofwater to pass through, which is visible in Figure 2-7(a), but that water was stopped by the tertiaryberm. The water flowing through the berm not only flooded the city but, by concentrating the waterflow velocity, caused scouring at the point of penetration and heaving as far as 150 m from thelocation of the failure, in the parking lot of an adjacent church building. Upon inspection of theberm during Wave 3a, it appeared that the sand used to construct the berms had not been
compacted during construction, which is likely due to the quick construction of the berm wascreated – the city did not have permission to build a berm over the CSX railroad until the state
governor signed an executive order days before the flood reached the city (Robesonian, 2018).
While the primary berm (dotted red line) was breached by the Lumber River, flooding many
residential areas of the city for a second time, the secondary berm (dashed yellow line) may have
prevented the reach of the flood by keeping the railroad and VFW Road intact. As pointed out by
the dashed white oval in Figure 2-6(a), a section of the railroad and VFW Road was washed out inthe 2016 flood, allowing water to spill into the northeastern areas of the city, between the leveeand railroad. Figure 2-8 shows a digital elevation model of Lumberton (USGS, 2017), with brown
to blue shades representing lower elevations and blue shades representing higher elevations, andcolor intensity representing increasingly extreme elevation in the direction of the shade. The
railroad, levee, and underpass locations are shown in Figure 2-8; the data indicates that the railway
structure is a local high point running completely across the southern area of the city. The
secondary berm built along VFW Road directed water south and west, providing protection to therailway infrastructure, and further preventing the region of the city north of the railroad and southof the levee from being flooded by the Lumber River.

26

Figure 2-8. Digital elevation model (USGS 2017) showing elevations in area of Lumberton
South of the levee, with CSX railroad and levee locations noted.
While some flooding did occur in this area in 2018, it was attributed to pluvial flooding alone as
opposed to the flooding that occurred in 2016 which was mostly attributed to riverine flooding thatentered through the I-95/CSX underpass and then through the undercutting of the railroad. Manyof the homes and businesses in this region were completely protected from flooding in 2018. Of
the homes from our sample that were flooded in this region, our team measured lower inundationlevels in Wave 3a compared to those assessed in Wave 1.
2.7.2 Flood Mitigation for Critical Infrastructure Systems
In addition to the berm system built at the I-95/CSX underpass, the City of Lumberton took
mitigating actions to protect its infrastructure systems. Figure 2-9 shows the Lumberton Water
Plant and an electrical power network substation in the leveed area of Lumberton. Both wereseverely flooded in 2016, which caused disruption to the water and power systems in Lumberton.
The loss of these utility systems was a major stressor on many of the residents in the areas servedby these systems and affected their recovery timelines (van de Lindt et al., 2018; 2020; Sutley etal., 2019). The Lumberton Water Plant, located south of the levee and north of West 5th Street, ismarked with dashed red circles in Figures 2-9(a) and (c). Water service was disrupted following
the 2016 flood (Armstrong, 2016) due to inundation of the water treatment plant as well as damage
to an intake pump and failure of the treatment plant’s primary generator. As shown in Figure 29(b), which provides the image from Figure 2-9(a) zoomed in for details of the plant, a protective
berm was built around the plant controls leading up to Hurricane Florence. This berm may nothave been needed to protect against the flood water in 2018 due to the protection of the I-95/CSX
underpass berm system, described earlier, which kept flood water out of the Lumber River fromentering that portion of the city. While the plant itself was not inundated in 2018, a well locatedoutside of the levee was inundated to a higher level in 2018 than in 2016, according to city officials,
resulting in a boil water advisory for those households in the water treatment plant's distribution
network.

27

Figure 2-9. Lumberton water plant and an electrical power network substation in the leveed areaof Lumberton that flooded in: (a) 2016 from Hurricane Matthew (NOAA, 2016) with (b) Closeup of Lumberton Water Plant, and (c) 2018 from Hurricane Florence (NOAA, 2018) with (d)Close-up of the electrical substation.
An electrical substation, denoted with yellow dotted circles in Figure 2-9(a) and (c), is locatedsouth of West 5th Street, on the eastern edge of the levee-protected area. The substation supplied
electricity to much of the population south of the levee and was inundated by the 2016 flood. The
City of Lumberton placed temporary flood barriers around the levee leading up to Hurricane
Florence, as shown in Figure 2-9(d), which provides a close-up of the image from Figure 2-9(c).
Flood waters reached the substation in 2018; the aerial imagery shows that the flood barriers
prevented water from inundating the substation in 2018.
2.7.3 Hydraulic Pumps
In 2018, the City of Lumberton deployed temporary hydraulic pumps in areas south of the leveeto quickly remove flood water from the leveed area and to protect critical infrastructure, should
inundation occur. Pumps were placed over the berm system built at the I-95/CSX underpass, overthe berm built at the Lumberton Water Plant, and over the east side of the levee. The pumps placed
over the levee and berm system likely reduced the amount of time that the flood water affected thebuildings inside the leveed area, potentially reducing the impact of the flooding. The pumps over
the Lumberton Water Plant berm were not needed as the building was protected from severe

28

flooding, likely due to the I-95/CSX berm system protecting the railroad tracks from being
undercut. High-water marks were more challenging to locate on structures in Wave 3a comparedto those observed in Wave 1, likely attributable to the reduced duration of inundation.

2.8 Observations of Households and Businesses: Individual-Level Preparation
and Response
In addition to changes in the community-level response between the two floods experienced byLumberton, individual-level responses also minimized risks to life and property. Most residents
were in their homes during the 2016 flood, which led to emergency evacuations of residents during
the flooding. Conversely, many of the residents and business owners, whom our team alsointeracted with in Wave 3a, acted before the flood, including evacuating during the days leading
up to the hurricane’s landfall.
Through virtual and in-person observations, combined with informal conversations with residents
and business owners during Wave 3a, various short- and long-term mitigation measures
implemented by Lumberton residents in preparation for Hurricane Florence (see Figure 2-10 for
examples) were identified. Short-term mitigation measures included placing sandbags at building
entrances, moving personal belongings, equipment and other items, such as important paperwork,
to higher ground indoors or outdoors, elevating HVAC units outside the home or relocating
ductwork from crawlspaces to attics, and deploying temporary flood barriers. Figure 2-10(a)shows a major international retailer that deployed temporary flood barriers (NOAA, 2018). Basedon an assessment of aerial imagery, the business pictured was the only one in the community, otherthan the electrical substation mentioned earlier, that deployed temporary flood barriers.
Many residential structures in Wave 1 were inundated into their crawlspaces, but did not flood up
to their first-floor elevation (FFE). No structural damage was apparent in such homes, but financial
losses were still substantial in comparison to the economic capacity of the households. Several
residents shared with the data collection team during Wave 3a that their ductwork had beenrerouted between Hurricanes Matthew and Florence, and corresponding differences in observed
damage was recorded in the damage survey. Similarly, during the Wave 3a damage survey,
residents and business owners shared that they had elevated their personal belongings,
merchandise, paperwork, and/or equipment to higher levels inside (e.g., placing contents on beds,
dressers, shelves, or upstairs). Lastly, the data collection team noticed that several air conditioning
condenser units had been replaced and elevated on top of pedestals after the 2016 flood. Figure 210(b) provides an example of an air conditioning condenser unit placed atop a concrete pedestal
at a public housing unit. These pedestals were typically constructed from wood, except outside apublic housing building where concrete pedestals were used. The heights of various air
conditioning condenser unit pedestals were measured between 0.91 m (3 ft) to 1.22 m (4 ft) above
ground level. Collectively, these simple actions likely resulted in significant reductions of property
losses for the residents who adopted them. They were also used to inform survey design for Wave
3c, as discussed in Chapters 3 and 4 of this report.

29

Figure 2-10. Examples of visible individual-level mitigation efforts in Lumberton implemented
before the 2018 Flood; (a) Temporary perimeter flood protection around international retailerbuilding (NOAA 2018); (b) Elevated air conditioning condensers in a public housing complex.

30

Chapter 3: Housing Disruption and Recovery
3.1 Goals and Objectives
The housing component of the Wave 3c field study is in support of on-going research in the Center,
including modeling housing damage and functionality loss, household dislocation, household
relocation, housing repair and recovery, and understanding mitigation and capacity-building
actions taken by households in Lumberton.
The goals of the housing data collection for Wave 3c include:● Complete missing information on initial damage not captured during Wave 3a;● Improve documentation on the duration of home repair experienced by households
considering different initial damage levels from Hurricane Florence, continued disrepair
from Hurricane Matthew, income levels, available recovery resources, and sociodemographic characteristics;● Develop a better understanding of what interdependent infrastructure systems and services
were lost during Hurricane Florence, and how households considered the restoration ofthese services in their timing and decision to leave and return home;● Obtain household-level data on a range of factors that are hypothesized to influence
housing recovery, including financial resources, as well as measure housing recovery
progress, including households’ housing stability and accessibility;● Obtain household-level data on resilience measures, including social capital change,
preparedness, and mitigation.
Wave 3c primarily utilized a structured questionnaire (survey) executed with residents considering
the longitudinal sample of 567 housing units used in Waves 1 and 2. This chapter presents the
housing sample design, data collection instrument, data collection processes, and findings from
Wave 3c, as well as a glimpse of longitudinal recovery across Waves 1, 2, 3a, and 3c.

3.2 Sampling Procedure
To continue the longitudinal study, Wave 3c utilized the same housing sample as Waves 1 and 2.
The school attendance zone for Lumberton Junior High, which includes the attendance zones forthe two elementary schools (W.H. Knuckles and Tanglewood Elementary Schools), defined thestudy area for Lumberton. A two-stage non-proportional cluster sample was designed to capture a
representative random sample of the study area, which included areas inundated by flooding aswell as areas that were not flooded. Within this sample design, the penultimate sampling units
were census blocks and primary sampling units were housing units and households residing in
those units. The penultimate sampling units (blocks) were selected utilizing a probability
proportion to size (PPS) random sampling procedure with blocks in areas with a high probabilityof flooding selected 3 to 1 over areas with low flooding probability. Areas subject to highprobability of flooding were identified as those inside the University of Alabama’s predicted floodinundation area or in FEMA designated 100- and 500-year floodplains (see Chapter 1 in van deLindt, Peacock and Mitrani-Reiser et al. 2018). A 100-meter buffer was added to the floodplain toaccount for uncertainty. Housing units within the sampled blocks were then selected at a fixed rateof 10 random units per block, 8 units were identified as primary with 2 alternates. The combination31

of selection PPS with a fixed number of housing units selected, after weighting, ensures a
representative sample of the area. Among the 830 blocks with five or more occupied housing units
in the school attendance zone, the sampling process drew a random sample of 80 blocks based ona probability of selection proportionate to size, oversampling for high probability flooding (56census blocks in the high probability areas and 24 in the low probability areas). In the final sample,
75 of 80 census blocks were visited (54 census blocks in the high probability areas and 21 in the
low probability areas), including 568 valid primary housing units, yielding an average of 7.6
housing units per block. For more detailed information about the sampling procedures for Wave
1, please refer to section 3.2.1 in the Wave 1 report (van de Lindt, Peacock, and Mitrani-Reiser etal., 2018).
The 568 housing units, and the households living in these units, were the primary sample units forWaves 1 and 2, and the target sample units for Wave 3c. During Wave 1, one household showeda very explicit rejection and accordingly, this housing unit was dropped entirely from the Wave 2
housing sample. Therefore, Wave 2 consisted of a selected sample of 567 housing units. Similarly,
Wave 3c started with this initial sampling frame from Wave 1. Wave 3a deviated slightly from thelongitudinal sample by adding new units suspected to be flooded by Hurricane Florence but notby Hurricane Matthew. More details about the expanded sample used in Wave 3a is provided in
Chapter 2 of this report.

3.3 Survey Instrument
Multiple researchers within the Center and NIST, each with unique expertise and interest incommunity resilience and disaster recovery, participated in developing the survey. Having a
multidisciplinary team helped ensure inclusion of different dimensions of household andcommunity recovery in conjunction with housing recovery and in the wake of successive
catastrophic flooding events. During development, the survey instrument underwent several
rounds of review both initially and through pre-testing and training by researchers within theCenter and at NIST, specifically in the Community Resilience Group of NIST’s Engineering
Laboratory (EL). Pretesting and training were two integral steps in the survey development
process. They were particularly critical to make sure questions were presented in an easy-to-follow
fashion and were very clear about which flooding event was being addressed.
The final housing survey instrument also went through review for the Paperwork Reduction Act(PRA) (1995, Pub. L. Count 104-13, 109 Stat 163). The purpose of the PRA review is to: “ensure
the greatest possible public benefit from and maximize the utility of information created, collected,
maintained, used, shared, and disseminated by or for the Federal Government;” and to “improve
the quality and use of Federal information to strengthen decision making, accountability, andopenness in Government and society.” The instrument and data collection methodology for thehousehold survey and the full Wave 3c Lumberton study were approved by the Institutional
Review Board (IRB) at Colorado State University, which oversees the Center’s human subjects
research.
The Wave 3c survey built upon the surveys utilized in Waves 1, 2, 3a, and 3b. Several questions
on household dislocation and socio-demographics were drawn from the Wave 1 survey, consistent
with those developed for the Wave 2 survey. Some sections were repeated verbatim from thoseprevious surveys (e.g. occupancy status) to increase the reliability of the longitudinal data on

32

dislocation. This also ensured reliability through using a similar procedure for verification ofoccupancy versus abandonment.
New sections of the survey were developed drawing from team expertise, and findings from Wave
3a. Team expertise was used to separate impacts caused by Hurricane Matthew from impacts
caused by Hurricane Florence. Findings from Wave 3a were incorporated by adding questions onhousehold-level preparedness and mitigation.
There are eight main sections in the survey. Those include (1) establishing occupancy and study
eligibility, (2) state of repair and recovery following Hurricane Matthew, (3) initial damage, utility
outage and repair progress caused by Hurricane Florence, (4) dislocation after Hurricane Florence,(5) recovery in-place after Hurricanes Matthew and Florence, (6) educational recovery, (7)household preparedness and mitigation, and (8) household socio-demographic characteristics. See
Appendix 3A for the full Wave 3c housing survey.
3.3.1 Establishing Occupancy and Study Eligibility
As in Waves 1 and 2, upon approaching a housing sample unit, surveyors observed the landscapefor safety, accessibility, and occupancy. There were two places to record information based onthese initial observations and based on the result of the survey if it was completed or not. The firstof these was titled ‘Result / Completion Codes,’ and included options for survey completion:
incomplete or partial completion; no answer or response but appears occupied; ineligibility
because no eligible person was available; ineligible because the structure is not a residence;
incorrect address; no access; or not occupied and appears abandoned or destroyed. Refusals wererecorded as no response but appeared occupied and a note was written in the comments to
document the refusal.
The second box was titled ‘Housing Unit Occupancy Status’ and included options similar to theprevious but wholly focused on occupancy, rather than survey completion. If the household was
not present but their neighbor or landlord was outside, or their neighbor was also part of the sample,
then the neighbor or manager was asked five questions related to occupancy and dislocation causedby the flooding.
If an eligible household member was present and provided verbal consent to be surveyed, then thefirst question was to determine how many people lived in the household — both adults and children
under 18 years old — and then determine when the household moved into the home. If thehousehold moved in after Hurricane Florence (after Sept. 2018), then the surveyor skipped to thevery end of the survey asking only socio-demographic questions. All households that moved inprior to Hurricane Florence were asked all survey questions. The scope of these questions is
explained as follows.
3.3.2 State of Repair following Hurricane Matthew
To understand the impacts of Hurricane Florence, it was first necessary to understand the state of
the home and the household immediately preceding the event, and thus the status of their repair
and recovery from Hurricane Matthew. These questions included asking whether the home wasoriginally damaged from Hurricane Matthew, and to what extent it was repaired before Hurricane

33

Florence. Subsequent questions, to be discussed in Section 3.3.4, asked about recovery progressfrom both Hurricanes Matthew and Florence.
3.3.3 Initial Damage, Utility Outage, and Repair Progress following Hurricane Florence
As discussed in Chapter 2, Wave 3a occurred just three weeks after Hurricane Florence with theintention to document the initial physical damage. In the Wave 3c follow up, it was imperative tounderstand impacts beyond initial damage. Similar to the procedure in Wave 2, in Wave 3cresidents were asked if their home was damaged from Hurricane Florence, and the current state of
repair. Similar to the procedure in Wave 1, Wave 3c respondents were asked about utility and otherservice outages, including electric power, water, wastewater, gas, cell phone service, landline
service, and internet connectivity disruptions. These questions on outages also captured the lengthof time of the disruption immediately after Hurricane Florence and included the duration of any“boil water order” or how long they thought water was unsafe for consumption (i.e., the service
may not have stopped, but was the water safe?).
3.3.4 Household Dislocation after Hurricane FlorenceThis portion of the survey asked questions regarding household dislocation timing, and factors
which may have contributed to their decision to return. Influencing factors considered in the survey
were modeled after the Wave 2 survey, including the timing of insurance payouts or other financial
assistance, closures in the resident’s place of work or children’s school or other businesses, suchas grocery stores. Residents were then asked if anyone in their household missed work due tohousing issues due to either Hurricane Matthew or Florence, the greatest number of workdays
missed, and the place of employment for the household member who missed the most workdays.
3.3.5 Housing Recovery In-Place after Hurricanes Matthew and Florence
The recovery in-place portion of the survey was designed to document how recovery was
progressing after Hurricanes Matthew and Florence, and what financial resources households had
available to them for that purpose. To start, households were first categorized as owners or renters,
and then asked what type(s) of insurance they had prior to Hurricane Florence and whether theyreceived payouts from any insurance after Hurricane Florence. If the household received a payout,
they were asked when they received the payout, and if it was sufficient in covering their repairs.
If the household reported they did not have insurance prior to Hurricane Florence, they were asked
if they had insurance at the time of the Wave 3c survey. Insurance questions were asked strictly
about Hurricane Florence, allowing responses from Wave 2 to provide information on insurance
coverage after Hurricane Matthew. The decision to exclude Hurricane Matthew questions related
to insurance payouts in Wave 3c were based on the timing to receive insurance payouts reported
in Wave 2 where the average for all three types of insurance were received before 90 days, although
six of the 75 reported payments were received after 90 days. As such, the field study teamdetermined it was unlikely any new insurance payouts were administered to households between
Wave 2 (14 months after Hurricane Matthew) and Wave 3c (two years after Hurricane Matthew).
The next set of questions asked about external resources available after the floods. In this case, therespondents were asked whether the household applied for, whether they received funds, and when
they received funds all since the beginning of 2018 for either Hurricane Matthew or Hurricane
Florence. The structure of this set of questions was driven by the fact that some of the federal
resources were delayed after Hurricane Matthew and only became available to households
34following Hurricane Florence. Thus, households could have received financial support after
Hurricane Florence for either flooding event. The types of resources considered in this series ofquestions included: FEMA home repair funds, Small Business Administration (SBA) loans; HUD
Community Development Block Grant- Disaster Recovery (CDBG-DR) home repair grants;
funding from FEMA’s Hazard Mitigation Grant Program for home elevation, buyout, demolition
or rebuilding; financial assistance from Non-Government Organizations (NGOs); clean up or
repair help from NGOs; financial assistance from friends or family; and cleanup help from friends
or family. Once recovery resource information was captured, respondents were then asked toconsider all of the assistance and insurance payouts they received and determine whether it wasenough to repair and replace all damages to their home and contents from both flooding events. If
the respondent answered no, they were asked what portion of the damages were covered (verylittle, some, or almost all/all), and whether they could pay for the rest from personal funds.
Following the questions on financial resources, respondents were asked about their recovery inplace, including whether they had the same access to school, work, grocery stores, and otheressential needs in their home now as they did before both flood events. Finally, respondents wereasked about their stability in their current home, and whether they intended to move in the nextyear, and if so, where.
3.3.6 Educational Recovery
The same two questions from Wave 2 were asked of respondents who indicated they had children,
including whether their child was enrolled in school in Lumberton in the past 12 months, and if
so, the status of their educational recovery.
3.3.7 Preparedness, Mitigation, and Social CapitalThis was the only new section of the survey. During Wave 3a, the field study team collected
anecdotal evidence from conversations with resident about different preventative actions they hadtaken before Florence in comparison to before Matthew. Also, during Wave 3a, the field study
team noticed differences in where the flooding occurred, and learned that the city deployed pumps,
and noted cases where air conditioning condensers had been elevated. These anecdotal
observations motivated systematic data collection on preparedness and mitigation actions taken byhouseholds and businesses. In Wave 3c, respondents were asked whether they had elevated their
hot water heater and/or air conditioning condenser, elevated interior contents before the flood, had
their home inspected by an engineer, rerouted ductwork from below the floor into the attic space,and/or developed a disaster plan with their household members. The respondents were asked
whether these actions were performed before Hurricane Florence, and whether they had performed
those actions/mitigations/changes at the time of the survey. There were also three lines on thesurvey marked as “other” allowing respondents to indicate any other type of preparedness and/or
mitigation action they took outside of those listed on the survey. To understand capacity building
beyond physical actions, respondents were also asked whether their household’s involvement withneighborhood and/or community groups, as well as with neighbors and extended family had
increased since Hurricane Matthew. These questions were used as proxies to understand changes
in social capital.

35

3.3.8 Household Socio-Demographic Characteristics
Questions on household characteristics were duplicated from the Wave 2 survey, and included
categorizing respondents as renters or owners, and obtaining information to categorize the highest
education level of any member in the household, racial makeup, ethnicity, and combinedhousehold annual income. These types of questions serve to document whether our sample
matched the distribution of socio-demographics indicated in 2019 ACS data (i.e., 5-year census
estimates). This information was also used to examine disparities in. dislocation, relocation, repair,and recovery processes due to household socio-demographics, as is expected due to social
vulnerability theory and what was observed in Waves 1 and 2.

3.4 Data Collection Methodology
The household surveys were conducted face-to-face by interviewer teams of two. An information
sheet about the field study and Center project were handed to households upon answering the door;
a consent script was used to obtain verbal consent prior to surveying (see Appendix 3B for the
information sheet and Appendix 3C for the consent script). The surveying took place April 11 to21, 2019, across both weekends, as well as during the weekdays with a more limited team allowing
the team to shift more personnel to business surveys during typical workday/week hours. In
addition to required institutional ethical human subjects training, all surveyors received teamtraining on ethics, survey use, planned field coordination, and best practices for in-field safety
prior to being in the field, and additional guidance and training when in the field. This section
provides the detailed description of pre-trip training, daily operations and in-field training, anddata management.
3.4.1 Daily Operations
Daily Process. Each morning, the team would convene to review logistics for the day, including
arranging teams, vehicles, drivers, creating a check-in schedule, retrieving packets of datacollection materials (e.g., surveys, photo release forms, supplemental information for respondents
such as local mental health resources, interview guides, personal protective gear), and preparing
for data collection. As needed, training sessions would be folded into this schedule toaccommodate new team member arrivals or shifts in team composition. Survey preparation
included review of the survey instrument, review of how the sample was clustered into smallerareas for team assignments, preparation of packets for each sample cluster, and discussion of issues
with survey questions, answer options, data recording in the field, and data entry.
Each evening, the full field study team would meet to enter and back-up data, update the sample
based on housing units visited that day, and review any issues from the day. At the end of eachday, the new data was entered into QualtricsXM software, licensed to the University of Kansas, by
the survey team. Similarly, the sample database was updated each evening with information
pertaining to which housing units were visited each day. This information was used to update thesample on Google Maps, and guide survey work for the following days, similar to what was donein Waves 1 and 2.
Although resources (e.g., staff, time, and funds) were limiting factors in data collection, several
actions were taken to improve the outcomes of the field study. To ensure a higher response rate to
the housing survey, the team:

36

●
●
●
●

Trained surveyors for maximum efficiency in the field;
Concentrated surveying on evenings and weekends;
Made repeat visits to housing units in the sample;
Arranged scheduled follow up times for households not available for surveying duringinitial visit (if willing to participate);● Adjusted the field work plan and team composition based upon daily evaluation of results.
Driving Assessments. Prior to surveying in a new cluster and as time permitted, the teams would
perform driving assessments of the sample clusters in order to identify all housing units that wereclearly unoccupied or abandoned. This assessment allowed for increased surveying efficiency on
the ground as time could be better concentrated on the housing units for which a response was
likely. The process was as follows: a team of at least 2, ideally 3, individuals would travel in asingle vehicle with one person driving, one navigating a route through the sample cluster, and athird (or the navigator) documenting the housing unit ID of each unoccupied or abandoned housing
unit and capturing a photo of the housing unit.
Times of Day. Emphasis was placed on concentrating survey or capacity on days and times when
response rates were expected to be highest. Weekend days, afternoons, and early evenings were
the focus of the effort, although there were teams that covered weekdays throughout the field study
period. Early mornings (before 9am), and Sunday mornings (before noon) were avoided to respect
cultural norms of the area. Teams completed their surveys before dusk for safety considerations.
Team Composition. Representation of multiple disciplines was a primary focus of Wave 1 teamcomposition. In Wave 2, the focus was on experience with surveying, knowledge of the area, andgender. A similar experience-based approach was used in Wave 3c. Other team composition
factors were driven by an awareness of the importance of safety and matching best practices forfield research in any location. Two teams of two would travel in a single vehicle and work throughone cluster together. A vehicle would be moved when the team would complete a small geographic
area (e.g., half of a block). The rule of thumb was to keep the vehicle where the surveyors could
still see it. While gender was used as a team-composition criterion to maximize interviewer safety,
the Wave 2 lesson that there was a higher success rate for teams of all female surveyors,particularly in higher-income and older-age neighborhoods was used to match teams to samplingclusters.
3.4.2 Data Management
All physical data is stored in a locked file cabinet and all electronic media is saved in locked offices
on the password protected computers of the principal investigators. A linked-list has been created
where all identifiable information was replaced with code numbers. The same codes are used forfield notes and photographs from each site. No names are attached to this documentation.
Photographs produced through the fieldwork that contain identifiable information are only viewed
by team members unless express written permission is provided by anyone identifiable in thoseimages. Original data access is limited to project investigators who have completed theInstitutional Review Board (IRB) training and whose universities have signed the Interagency

37

Agreement (IAA). The data will be maintained for the three-year archive period following theconclusion of the study and will be shared with NIST.
Documenting survey responses. Data collection was completed using fillable paper-based survey
forms. Data collectors carried all survey materials on clip boards including answer cards for selectitems (e.g., income, see Appendix 3A), project information sheets (see Appendix 3B), andresources for respondents (e.g., financial assistance information, see Appendix 3D), and. The
survey forms included the housing unit ID as the primary identifier; surveyors were trained torecord the ID on each page of the form in the event that pages were separated. Data was collectedin accordance with the surveyor training. Any additional notes or cleaning of the survey form wascompleted before beginning the next survey. Surveyor guidance was included in the survey form
to support the proper elicitation and recording of responses when in the field. Survey teammembers also discussed and verified answers with one another before moving to the next unit in
the sample to verify that the data on the form was accurate.
Photographs. While less damage and debris were visible during the Wave 2 data collection
compared to Wave 1, Hurricane Florence in September 2018 caused new damage and debris to a
portion of the housing sample. Although limited, pictures taken during Wave 3c captured some of
the new damages from Hurricane Florence as well as the unoccupied housing units that had notexperienced any repair or reconstruction since Hurricane Matthew and/or Hurricane Florence.
Daily procedures of entry and back up. A number of steps were completed each day in order toensure that all data were managed appropriately and in accordance with IRB protocol [15-6003H].
Each evening, upon return from the field, the team would complete the data entry of all survey
forms recorded on that day, which included complete surveys as well as incomplete surveys withresponse code information filled in. The information would most often be entered by a member ofthe pair that directly recorded the data. All hardcopies of the survey collection forms were thenstored by the field study leads.
3.4.3 Data Cleaning Process
In-field data entry for Wave 3c housing survey was first tested with Wave 2. Having the team enter
data at the end of the day proved to be extremely valuable as issues with the electronic survey form
were quickly resolved and the electronic form could be fixed on the fly. In addition, if a teammember had a question about what they wrote down on the form, they can directly discuss an issue
with other team members and decide what they should enter on the electronic form. This enabled
the team to increase the validity of the data and reduce human errors during the data entry process.
The data were entered using Qualtrics and stored on the cloud server provided through Texas A&M
University. Data were also entered in Google Sheets in order to update the sample map.
In addition to doing data entry in the field, data cleaning was also initiated during the field study.
This was done by merging the Google Sheet with the Qualtrics CSV file and checking forinconsistencies. After merging, five types of issues were checked, including:
1. Incorrectly typed housing unit IDs
2. Double entered survey forms
3. Missing entries – surveys not entered38

4. Second visit surveys not entered
5. Issues with inconsistencies in the use of completion codes
These data cleaning issues were resolved at the end of the day with team members familiar withthe data collection, fixing issues on the spot. All changes for Qualtrics were recorded in a Stata Do
file. After the field trip, the data entry was double-checked with the original hardcopy of the survey
to identify any typographical errors, and any logical inconsistency.
Labeling in Qualtrics. To make it easier to create the desired Stata Dataset, having the codes forvariables in the labeling was efficient. For example, instead of “Yes,” substituting “1. Yes” madethe conversion from Qualtrics to Stata easier. It also helped ensure consistency in the coding. It is
possible to edit the labels in Qualtrics after the data entry without impacting the data. But editing
survey questions in Qualtrics should only be done to add the numerical code and not change anyof the ordering or content. In addition, in order for Stata to process all possible answers for eachquestion, sample entries with preview mode were completed by selecting each possible answer.
Reformatting Temporal Data. The formatting of temporal data (length of time in days, weeks,
and months, for example) has been found to be a consistent problem across survey waves. During
Wave 3c some data were still collected by respondents with different formats, such as calendar
date or verbal description. In addition, not every respondent remembered the exact date ofdislocation or a number of days utilities were out of service, so respondents provided partial
information of dates during the survey. In this case, several rules were applied to obtain consistentestimates. When converting string variables to numeric variables with days format, all descriptions
from comments, verbal description, and other information collected from the survey in the field
were used to estimate the date. Timing issues generally occurred in questions related to “theimpacts of Hurricane Florence on utilities” and “the insurance payouts.” The same rules wereapplied to the data cleaning process for all time-related questions.
First, similar to the Wave 2 data cleaning process, when a range was recorded for the timing of
events, the higher value was taken (e.g., 2 to 3 months was recoded as 3 months in the final dataset).
Second, when the exact date was not recorded and only the month was recorded on the survey orin Qualtrics, the midpoint day for MM/YY information was used. For example, if March/2017 wasprovided as the day of insurance payout after Hurricane Matthew, the team assumed it meant
March 15, 2017, and calculated days from October 10, 2016 when Hurricane Matthew flooding
occurred in Lumberton. In addition, if the year the household moved to the unit was roughly
provided as 1970s in Qualtrics, the rough range (1970s) was converted to 1975 to calculate years
lived in the unit.
Third, although most timing questions were intended to be answered as a number of days, some
respondents answered with a specific date. Therefore, the date had to be converted to days bysubtracting the answer date to the day of the hurricane events. For consistency, the team came toa consensus to fix October 10, 2016 for Hurricane Matthew to calculate temporal variables since
the flood depth peaked on that day in Lumberton; this date has been consistently used by the fieldstudy team since Wave 1. For Hurricane Florence, September 17, 2018 was chosen because heavy
rain caused widespread inland flooding on that day and mandatory evacuation was announced on
September 16, 2018 in South and West Lumberton.

39

Fourth, if the month was the only information provided by households and it was the same monththat Hurricane Florence occurred, the team assumed it lasted approximately 15 days. For example,
if the household answered the insurance payout after Hurricane Florence was received in “9/2018”,
it was converted to 15 days by subtracting the peak date of Hurricane Florence from the end of
September 2018.
Lastly, if the household answered the utilities are “still not repaired” or the insurance payouts are“still not received” it was recorded as missing to differentiate from either the most recently repairedor received date, which would be the longest days in the variable. In addition, flag variables werecreated and used to differentiate them from “not-responded” missing values.

3.5 Housing Survey Results
The data for housing recovery were analyzed independently, though it will contribute later to abroader suite of analyses as part of the longitudinal study for Lumberton, NC. This section presents
the housing survey summary statistics resulting from Wave 3c. Analyses include basic descriptive
statistics and visual presentation of the data. Detailed and comprehensive analyses including
empirical modeling will be presented outside of this report in peer-reviewed articles. Resulting
publications
will
be
made
available
ontheCenterwebsite(http://resilience.colostate.edu/publications.shtml).
3.5.1 Survey Response Rates
Data was collected from three types of interviewees: continuous residents, new residents, and when
the resident was not available, a neighbor or manager was sought for limited information. New
residents are those who moved into the housing unit after Hurricane Florence and the subsequent
flooding. Continuous residents are those who lived in the housing unit before Hurricane Florence,
and were asked the longest set of questions on the survey.
Survey completion rates are reported in Table 3-1 as ineligible, abandoned, no answer, refusal,
and completed surveys. Ineligibility could have occurred due to having no access to the property(e.g., gated fence, dangerous crossing), or when a non-residential property appeared in the sample
due to original miscoding or property changes since the last field study. Abandoned sample units
were marked when there was an existing residential unit on the property with evidence ofabandonment. This evidence was based on either the surveyor’s observation or consultation witha neighbor. No answer was marked when the unit was visited, and (1) it did not appear to beabandoned, but there was no answer at the door by the resident and no consultation with a neighboror manager, or (2) a child answered the door and there was no adult available. Refusals include
those households who answered the door but declined to speak with the survey team. Completed
surveys are reported for those sample units where surveyors were able to complete at least a portionof the survey with them.
The rate of completed surveys is provided for the three types of interviewees in Table 3-1.
Response rates by housing type are provided in Table 3-1. When subtracting the ineligible sample
units from the original housing sample (n = 549), and counting abandoned sample units as
responses, the overall response rate was 71 %. Without considering abandoned sample units as
responses, the response rate is 38 %. When only considering completions by new and continuous

40

residents, the response rate was 34 %. These latter two figures are not incorporated into Table 3-1
but provide reassurance of a successful data collection.
Table 3-1. Housing survey response rates.
Housing
Type

SingleFamily
MultiFamily
Manuf.
Home
Other /
Not
Specified
Total

SampleIneligible

AbandonedNo
AnswerRefusalCompletion
by
Neighbor

Completion
by New
Resident%(Count)%(Count)%(Count)%(Count)%(Count)%(Count).

%(Count)

76 %
(431)
19 %
(107)
2%
(14)
3%
(15)

94 %
(17)

79 %
(110)
19 %
(26)

85 %
(17)
10 %
(2)

62 %
(15)
38 %
(9)

-

-

11 %
(3)
81 %
(21)
8%
(2)

6%
(1)

76 %
(134)
14 %
(25)
4%
(8)
6%
(10)

2%
(3)

5%
(1)

-

-

-

100 %
(567)

100 %
(18)

100 %
(177)

100 %
(139)

100 %
(20)

100 %
(26)

100 %
(24)

100 %
(163)

-

-

Completion
byContinuous
Resident%(Count)Response
Rate

83 %
(135)
15 %
(24)
2%
(4)

69 %
74 %
100 %
66 %
71 %

3.5.2 Findings: Hurricane Florence Flood Impacts and Recovery
This section presents descriptive statistics on housing disruption caused by Hurricane Florence,and continued housing recovery from both Hurricane Matthew and Hurricane Florence. The
statistics provided are based on the proportioned sample of 861 housing units and will focus on:
the damage to the residential buildings; dislocation and duration of the dislocation for each
impacted household; causes of delay in households returning home; access to public services posthurricane; reported possibility of re-location; perceived educational recovery status for thoseresidents with children; duration of missed work; and, mitigation efforts pre- and post-hurricanes.
Different numbers of households responded to individual questions, hence all reported numbers
and percentages are out of the re-proportioned sample of 861, unless otherwise stated.
Damage and Functionality Loss
Table 3-2 provides the resident-reported damage caused by Hurricane Matthew. This factor wasnecessary to understand and control for in order to investigate disruption and recovery from
Hurricane Florence. Of the 299 households providing information about home damage caused by
Hurricane Matthew, 107 (36 %) are located within the inundation area. A total of 137 (46 %)households reported damage from Hurricane Matthew, 138 (46 %) reported no damage from
Hurricane Matthew, and 24 (8 %) households did not know if any damage occurred. Table 3-2
also breaks responses to this question by housing type, where a fairly equal proportion of singleand multi-family units were damaged (45 % and 47 %, respectively) and undamaged (49 % and
36 %, respectively), albeit substantially fewer multi-family home residents were surveyed. Five
manufactured home residents completed surveys, where 4 of them indicated that their home wasdamaged by Hurricane Matthew. When asked to approximate the amount of damage that was
repaired prior to Hurricane Florence, 131 households responded, where 88 (67 %) replied that“All” damage was repaired, 12 (9 %) said “Most” of the damage was repaired, 19 (15 %) said

41

“Some” damage was repaired, and 12 (9 %) said “None” of the damage to the housing unit was
repaired.
Table 3-2. Hurricane Matthew-induced damage to home.
By housing type:
Home damaged fromCount
SingleMultiManufactured
Hurricane Matthew?
(%)
FamilyFamily
Homes

Other

Yes

137
(46 %)

111(45 %)

22(47 %)

4(80 %)-

No

138(46 %)

119(49 %)

17(36 %)-

2(100 %)

Don’t know

24
(8 %)

15
(6 %)

8
(17 %)

1(20 %)

-

299

245

47

5

2

n=

As shown in Table 3-3, when asked if the housing unit was damaged by Hurricane Florence, 132(44 %) of the 299 respondents claimed to have suffered damage, while 7 (2 %) did not know. The
remaining 160 (54 %) of the responding households claimed no damage from Hurricane Florence.
When separated by housing type, a lower proportion of multi-family housing units reported
damage compared to single-family and manufactured homes. Looking across Tables 3-2 and 3-3,
we can see that 6 more single-family homes, 12 fewer multi-family homes, and the same numberof manufactured homes were damaged in Hurricane Florence compared to Hurricane Matthew.
Figure 3-1 provides the distribution of the number of days it took to repair the home reported by
the residents. For the 132 households reporting damage, the unit was repaired an average of 60.3
days after Hurricane Florence, while 51 (39 %) reported that the unit still had not been completely
repaired. Seven (5 %) provided no answer and 3 (2 %) did not know the timeframe of repairs totheir housing unit. Looking at Figure 3-1, 11 (8 %) damaged housing units required one and two
weeks to complete repairs, whereas 19 (14 %) took between one and two months, and 12 (9 %)took 60 to 120 days and another 12 homes took 121 to 240 days to complete repairs.
Table 3-3. Hurricane Florence-induced damage to home.
Home damaged from
Hurricane Florence?

By housing type:
Count
(%)

SingleFamilyMultiFamily

Manufactured
Homes

Other

Yes

132
(44 %)

117(48 %)

10(21 %)

4(80 %)

1(50 %)

No160(54 %)

125(51 %)

34(72 %)

1(20 %)-

Don’t know

7
(2 %)

3
(1 %)

3
(6 %)

-

1
(50 %)

299

245

47

5

2

n=

42

Figure 3-1. Number of days to repair home.
When asked if the respondent’s housing unit lost electrical power, water, potable water, sewer,
and liquified petroleum (LP) gas, researchers received 288 responses shown in Table 3-4 separated
by tenancy, where the category “Other” includes six respondents who did not declare whether theyowned or rented their housing unit. Of the 227 (79 %) respondents who lost electrical power after
Hurricane Florence, 163 were homeowners and 61 were renters. Proportionately more
homeowners lost power (859 %) compared to renters (68 %). Of the 113 (39 %) who lost water
after Hurricane Florence, 86 were homeowners and 27 were renters. Approximately half as many
households lost water as compared to power, including 45 % of homeowners and 30.0 % of renters.
While water supply is important, so is its quality. Respondents were also asked whether their water
was safe to drink once it was flowing again; 161 (56 %) reported their water was safe to drink,including 60 % of homeowners and 50 % of renters. Wastewater service interruption was not as
widespread as water service disruption. When asked whether their sewer stopped working, 86 (30%) reported that it did go out, including 33 % of homeowners and 26 % of renters. The loss of
propane or LP gas to a housing unit can have impacts for residents ranging from losing the ability
to cook or bathe to having no heat in the home. Thus, finally, residents were asked if their LP gas
service stopped and 33 (11 %) reported that it did go out, where 116 (40 %) of the respondents
reported not having this service to begin with. Homeowners were nearly twice as likely to lose LP
gas as compared to renters (14 % compared to 8 %). Overall, a higher proportion of homeowners
lost all utility services reported in Table 3-4 after Hurricane Florence.

43

Table 3-4. Hurricane Florence-induced utility outage and duration by tenancy.
Tenancy

Owners(n=192)Service
Outage?

Lose
power?
Count (%)

Lose water?
Count (%)

Yes

163
(85 %)

86(45 %)

116(60 %)

63(33 %)

26(14 %)

No

19(10 %)

76(40 %)

51(27 %)

104(54 %)

79(41 %)

Don’t
know

10
(5 %)

29
(15 %)

25(13 %)

24(13 %)

21(11 %)-

1(0.5 %)-

1(0.5 %)

66(34 %)

Yes

61(68 %)

27(30 %)

45(50 %)

23(26 %)

7
(8 %)

No

16
(.8 %)

39(43 %)

13(14 %)

44(49 %)

27(30 %)

Don’t
know

10
(11 %)

21(23 %)

26(29 %)

20(22 %)

6(7 %)No answer

3
(3 %)

3
(3 %)

6
(7 %)

3
(3 %)

50
(56 %)

Yes

3(50 %)

-

-

-

-

No

-

3
(50 %)

3(50 %)

3(50 %)

3(50 %)

Don’t
know

3
(50 %)

3(50 %)

3(50 %)

3(50 %)

3(50 %)

No answer

Renters(n=90)

Other
(n=6)

Water unsafe
Lose sewer?
to drink?
Count (%)
Count (%)

Lose LP
gas?
Count (%)

Cellular, landline, and internet connectivity were all impacted by Hurricane Florence. Table 3-5
shows 288 responses on telecommunication outages separated by tenancy, where the category“Other” includes six respondents who did not declare whether they owned or rented their housing
unit. In total, 58 (20 %) of respondents reported that they lost cell phone access, 80 (28 %) lostlandline access, and 119 (41 %) lost internet access. The loss of cellular service impacted fewer
homeowners (13 %) than renters (33 %). The loss of landline phone service saw a reversal of theloss of cellular service statistics in that more (34 %) homeowners experienced a loss of landline
telephone service compared to renters (13 %). When considering the loss of internet connectivity,
a closer alignment between the two tenancy groups is apparent. For homeowners, 45 % lost internet
connectivity whereas 36 % of renters lost internet connectivity.

44

Table 3-5. Hurricane Florence-induced cellular and landline phone outage and duration bytenancy.
Tenancy

Owners(n=192)Renters(n=90)

Other
(n=6)

Service
Outage?

Lose cell phone access? Lose landline access?
Count (%)
Count (%)

Lose internet
access?
Count (%)

Yes

25 (13 %)65 (34 %)

87 (45 %)No

129 (67 %)

50 (26 %)

39 (20 %)Don’t know

17 (9 %)

21 (11 %)

21 (11 %)No answer

21 (11 %)

56 (29 %)45 (23 %)Yes

30 (33 %)

12 (13 %)32 (36 %)No

42 (47 %)

23 (26 %)

15 (17 %)Don’t know

9 (10 %)

11 (12 %)

16 (18 %)No answer

9 (10 %)

44 (49 %)

27 (30 %)Yes-

3 (50 %)-

No

3 (50 %)-

3 (50 %)

Don’t know

3 (50 %)

3 (50 %)

3 (50 %)

No answer---

Figure 3-2 provides a boxplot for the outage duration of utility services that were reported in Tables
3-4 and 3-5. The red horizontal line represents the median, the lower and upper horizontal lines ofthe blue box mark the 25th and 75th percentiles, respectively, the black horizontal lines mark theminimum and maximum, and the red plus-signs mark the outlier values. The median outage time
was 5, 7, 8, 7, 14, 7, 7, and 7 days for power, water, sewer, boil order, LP gas, landline phone, cell
phone, and internet, respectively. All utility service outage durations had outliers, and all 75th
percentile values were 30 days or less. In two cases, LP Gas and Internet, the medians correspondto the 25th or 75th percentile values due to the nature of the data. Customers experienced the longest
outage times for LP gas service, and the shortest for power.

45

Figure 3-2. Utility service outage time.
Dislocation Time and Decision-Making
Housing dislocation after Hurricane Florence is presented in Table 3-6 with 178 of the 282
respondent households (63 %) reporting spending at least one night away from their home after
Hurricane Florence. Approximately 33 % of renters and 25 % of occupants of multi-familydwellings were dislocated relative to homeowners and occupants of single-family dwellings,
respectively. While 85 % of renters reported dislocating from their homes, only 53 % of
homeowners were dislocated. Although a small number of occupants of other building types
answered this survey question, 50 % of manufactured home residents were dislocated after
Hurricane Florence. The average number of days of dislocation was nearly twice as long forhomeowners (25.3 days) when compared with renters (13.3 days) with the duration of dislocation
reported by homeowners having a higher variance compared to renters. Similarly, averagedislocation duration was longer for occupants of single-family homes (21.2 days) compared tooccupants of multi-family dwellings (15.9 days) and manufactured homes (4.5 days) with pretty
similar standard deviation for single- and multi-family dwellings and less so for manufactured
homes. Median days of dislocation were relatively consistent for all housing and tenancy types,
except for manufactured homes and the Other category, which were both shorter.

46

Table 3-6. Household dislocation and duration by housing type following Hurricane Florence.
Count
Average
MedianStd. Dev.
(%)(days)(days)(days)
Housing
Single137
Type
Family21.2
1032.2(59 %)(n=232)Multi-Family
37
15.9
10
29.2
(n=44)(84 %)
Manufactured
2
4.5
4.5
3.5
Home (n=4)(50 %)
Other
2
13
3.5
11.3
(n=2)
(100 %)
TenureRenter
74
13.3
10
22.2
(n=87)(85 %)
Owner
101
25.3
11
36.6
(n=192)(53 %)
Other
3
8.0
8
0
(n=3)
(100 %)
Figure 3-3 presents the distribution of dislocation time by tenancy. As shown in Figure 3-3, 104
households did not dislocate after Hurricane Florence, including 91 homeowners and 13 renters.
Of the 178 households who were dislocated from their homes, the majority, comparable numbersof homeowners (39 and 25) and renters (34 and 32) were dislocated for one and two weeks. In
total, 29 homeowners and 8 renters were dislocated for longer than two weeks.

Figure 3-3. Dislocation time after Hurricane Florence.
Researchers asked respondents four questions to understand the issues that hindered the return totheir homes. These questions were framed around the timing of insurance payouts, the operational
status of their place of employment, and school and business closures. The results are provided in47

Table 3-7. Overall, these factors influenced the decision to return for small portions of households
in our sample. The largest proportion of respondents (15 %) answered that the timing of insurancecompany payouts or financial assistance delayed their return to their home. When asked if a changeor closure of their workplace caused a delay in returning to their home, 12 % responded that it did.
Smaller proportions, 8 % and 9 % of respondents indicated that their child’s school closure andchanges with other businesses in town caused a delay in their return to home after Hurricane
Florence.
Table 3-7. External influences on decision to return home after Hurricane Florence.
Timing ofChildren’s
Changes withWork changes
Influence yourassistanceschool changes other businesses
decision to return
Count
Count
Count
Count
home?
(%)
(%)
(%)
(%)
Yes

22
(15 %)

16(12 %)

9
(8 %)

13
(9 %)

No

129
(86 %)

118(88 %)

107(92 %)

134(89 %)

-

-

1
(1 %)

3
(2 %)

151

134

117

150

Don’t known=Dislocation has ripple effects in the lives of the individual and household functions. When asked
if any household members missed any time at work, 74 (26 %) of the respondents reported thatmembers of their household had to miss work due to flooding since the beginning of 2018, while
105 (37 %) did not miss any work, one did not know and 105 (37 %) did not answer the question.
Figure 3-4 presents the distribution of the reported work days missed by any household member.
Overall, 47 (63 %) of those who missed work missed one week or less of work. One household
had an individual miss 120 days of work due to housing issues since the beginning of 2018.

48

Figure 3-4. Number of days household member missed work since the beginning of 2018 due tohousing issues caused by flooding.
Housing Recovery Progress
Stability and accessibility were introduced as housing recovery metrics during Wave 2 (Sutley,
Dillard, and van de Lindt et al. 2021). Stability is measured as a household intending to stay intheir current home for at least one year. Accessibility is measured by whether the householdindicates they have the same access to essential needs, including work, school, and grocery stores,in their current home now as they did before the flood event(s). In Wave 3c, households were again
asked whether they planned to move from their home due to hurricane related reasons within thenext year and 22 of the 192 homeowners (11 %) answered “Yes” compared to 20 of the 90 renters(22 %) also said that they intended to move. Of those homeowners responding in the affirmative,
13 of the 22 said that they planned on moving “somewhere else in North Carolina.” Of the renters
responding yes to this question, 15 of the 20 (75 %) said they planned to move “somewhere else
in North Carolina.”
In Wave 3c, households were again asked whether they had the same level of access to theirchildren’s school, work, and grocery stores at present compared to before Hurricane Matthew and
Hurricane Florence. The results are presented in Table 3-8, where 32 %, 52 %, and 80 % of
homeowners indicated increased access to school, work, and grocery stores, respectively. Similar
proportions of renters indicated increased access to school, work, and grocery stores, including 40%, 49 %, and 74 %, respectively. As shown in Table 3-8, larger portions of surveyed households
did not respond to the question about school access given that they did not have children living in
the home.

49

Table 3-8. Change in household access to essential needs after Hurricane Matthew.
Tenancy

Owners(n=192)Renters(n=90)

Other
(n=6)

Increased since
Hurricane Matthew

School?
Count (%)

Work?
Count (%)

Grocery Stores?
Count (%)

Yes

61 (32 %)

99 (52 %)

153 (80 %)No

5 (3 %)

10 (5 %)

34 (17 %)Don’t know

8 (4 %)

5 (3 %)-

No answer

118 (61 %)

78 (41 %)

5 (3 %)Yes

36 (40 %)44 (49 %)

67 (74 %)No

6 (7 %)

7 (8 %)

8 (9 %)

Don’t know

2 (2 %)

2 (2 %)

1 (1 %)

No answer

46 (51 %)

37 (41 %)

14 (16 %)

Yes

-

-

3 (50 %)

No

-

-

-

Don’t know

-

-

-

No answer

6 (100 %)

6 (100 %)

3 (50 %)

Educational Recovery
Based on 285 survey responses, 85 (30 %) households reported having at least one person under
the age of 18 living in the household at the time of the Wave 3c survey. If there were children in
the household, researchers asked if those children were enrolled in a Lumberton district school in
the last 12 months. Eighty-two of the 85 (97 %) responded to the question, of which 78 (95 %) of
the respondents replied in the affirmative. For those 78 respondents, a multiple-choice question
was presented asking about the perceived trajectory of the children’s educational recovery
following both Hurricanes. The majority of respondents had a positive view about their child/ren’s
educational recovery with 2 (3 %) and 52 (67 %) reporting that their child/ren’s educational
recovery was better than and back to where it was prior to the two Hurricanes, respectively.
Approximately 22 % reported that their child/ren’s educational recovery trajectory was worse than
before the Hurricanes, and 9 % said they were uncertain.
Recovery Resources and Unmet Needs
There are many recovery resources that become available to households after disasters. Table 3-9
presents insurance coverage and claim payment timing after Hurricane Florence. Eighty-one
percent of respondents had an active homeowner’s insurance policy, 26 % were covered by flood
insurance, and 11 % were covered by renter’s insurance. Looking at those covered by theindividual types of insurance, 16 % of the 156 who had homeowner’s insurance were paid for a50

claim, 47 % of the 49 who had flood insurance received a claim payment, and 30 % of the 10 who
had renter’s insurance received a claim payment after Hurricane Florence. The average number ofdays to receive insurance claim payments after Hurricane Florence were approximately 37 days,
39 days, and 7 days for homeowner’s, flood, and renter’s insurance, respectively, with the greatest
variation in waiting time for flood insurance claim recipients.
Table 3-9. Insurance coverage, insurance payment receipt, and timing after Hurricane Florence.
Homeowner’s
Flood
Renter’s
Insurance
Insurance
Insurance
Count (%)

Count (%)

Count (%)

Had coverage

156 (81 %)49 (265 %)

10 (11 %)Received payment

25 (16 %)

23 (47 %)

3 (30 %)

Average days to receive
payment

36.6

39

7

Std. dev. days to receive
payment

27.9

42.8

0

Table 3-10 provides the proportion of households surveyed who applied for different types ofassistance. Considering who did apply, Table 3-11 provides the proportion of households who
received different types of assistance and when they received it after Hurricane Matthew. Table 312 provides the same information about who received assistance and what kind and when after
Hurricane Florence. As shown in Table 3-10, the highest proportion of survey respondents applied
for Federal Emergency Management Agency (FEMA) Individual and Household Program (IHP)
funds (61 %), followed by asking or applying for cleanup help from NGOs (43 %), and followedby asking friends and family (F&F) for help with cleanup (32 %). The smallest portion of
respondents applied for Department of Housing and Urban Development (HUD) funding (9 %)and FEMA Hazard Mitigation Grant Program (HMGP) funds (11 %). In general, as evident from
Table 3-10, a significant portion of affected households did not apply for external recovery
funding.
Of those who did apply for funding, the highest proportion received FEMA IHP funds (97 %),following by cleanup help from friends and family (80 %) after Hurricane Matthew. After
Hurricane Matthew, the lowest recovery funding rate was with HUD (38 %) and NGO funding (39%). After Hurricane Matthew, FEMA IHP funding took 138 days on average, whereas NGO
funding took 165 days on average to arrive. Notably, cleanup help and financial assistance fromfriends and family were much quicker than other recovery help and came in during the first monthafter Hurricane Matthew. Although some households were approved for FEMA HMGP and HUD
recovery assistance, few households had received either at the time of the Wave 3c survey.
Differently, of those who did apply for funding after Hurricane Florence, the highest proportion
received NGO funding (74 %), followed by cleanup help from NGOs and funds from friends andfamily (both at 53 %). FEMA HMGP (20 %) and HUD (13 %) had the lowest rates of approved
applications. Funding distribution was faster, on average, after Hurricane Florence compared to51

Hurricane Matthew. FEMA IHP and NGO funding took an average of 48 days, and funding fromfriends and family took 23 days on average after Hurricane Florence.

Applied/asked
Did not
apply/ask
Total
responding

Table 3-10. Household recovery resource inquiries.
FEMA
FEMA
SBA
HUD
NGO
NGO
F&F
F&F
IHP
HMGP Count Count funding cleanup funding cleanup
Count
Count
(%)
(%)
Count
Count
Count
Count
(%)
(%)
(%)
(%)
(%)
(%)
69
10
22
8
23
45
16
30
(61 %)(11 %) (22 %) (9 %) (24 %) (43 %) (17 %) (32 %)
44
83
77
85
73
60
77
64
(39 %)(89 %) (78 %) (91 %) (76 %) (57 %) (83 %) (68 %)
113

93

99

93

96

105

93

94

Table 3-11. Resources received and timing of receipt after Hurricane Matthew.
FEMA
FEMA
NGO
NGO
F&F
IHP
HMGP
SBA
HUD
funding
cleanupfunding
Count
Count
Count
Count
Count
Count
Count
(%)
(%)
(%)
(%)
(%)
(%)
(%)
Received
67
6
13
3
9
21
24
Resource(97 %)(60 %) (59 %) (38 %)(39 %)(47 %)(80 %)(Count (%))
Total
households
69
10
22
8
23
45
30
applying/asking
Average days
to receive
137.7
203.3
165.3
23.3
24.5
resource
Std. dev. days
to receive
237.0
117.8
151.7
22.8
51.9
resource52

Table 3-12. Resources received and timing of receipt after Hurricane Florence.
FEMA
FEMA
NGO
NGO
F&F
SBA
HUD
IHP
HMGP
funding
cleanupfunding
Count
Count
Count
Count
Count
Count
Count
(%)
(%)
(%)
(%)
(%)
(%)
(%)
Received
32
2
5
1
17
24
16
Resource(46 %)(20 %) (23 %) (13 %)(74 %)(53 %)(53 %)(Count (%))
Total
households
69
10
22
8
23
45
30
applying/asking
Average days
to receive
47.5
59
102
47.5
23.1
resource
Std. dev. days
to receive
45.4
0
34.9
50.4
52.4
resource
Respondents were asked to consider all of the types of recovery resources listed in Tables 3-9
through 3-12 and determine whether the resources were sufficient to repair and replace all physicaldamages to their home and contents caused by both Hurricanes Matthew and Florence. As shownin Table 3-13, 26 % indicated that they did receive sufficient funding to complete repairs andreplacement, whereas 67 % reported unmet needs. Of those with unmet needs, 83 % indicated theirunmet needs were covered using personal funds. Respondents were also asked to provide theapproximate proportion of their repairs covered by recovery resources. Of the 48 who responded
to this question, 39 % indicated very little, 54 % indicated some, and 6 % indicated almost all of
their repairs were covered (results not shown).

Yes
No
UnsureTotal responding

Table 3-13. Covering repair and replacement costs.
External Funds Covered Everything
Paid for Rest from Personal
Count (%)
Funds
Count (%)
19 (26 %)
40 (83 %)
49 (67 %)
7 (15 %)
5 (7 %)
1 (2 %)
73
48

Preparedness and Mitigation, and Social Capital
Researchers asked a series of questions about changes in social interactions and individual
preparedness and mitigation actions taken as one aspect of understanding household- andneighborhood-level capacity. These responses are presented in Table 3-14. When asked if the
impacts of Hurricane Matthew had increased their community involvement, roughly one-third of
homeowners (64 out of 192, 33 %) and renters (30 out of 90, 33 %) alike responded that the eventsof Hurricane Matthew increased their community involvement. When asked if the events of53

Hurricane Matthew increased their contact with neighbors or extended family, 72 out of the 192(38 %) of the homeowners and 36 out of the 90 (40 %) renters agreed that the events of Hurricane
Matthew had increased their contact with neighbors or extended family members.
Table 3-14 presents results from a series of prompts regarding actions taken by households either
before Hurricane Florence or at the time of the Wave 3c survey (after Hurricane Florence. Varying
numbers of households responded to each portion of each prompt, so percentages are not provided.
In total, 32 households elevated their hot water heater and/or HVAC before Hurricane Florence,and 45 (including the 32 reported above) had an elevated water heater and/or HVAC at the timeof the Wave 3c survey. More households (59 before Hurricane Florence and 71 at the time of thesurvey) had elevated interior contents in preparation for the flood. Fewer (25 before Hurricane
Florence and 33 at the time of the survey) had their home assessed by a structural engineer. The
fewest households (15 before Hurricane Florence and 24 at the time of the survey) had re-routed
ductwork from below the floor of their home to the attic space above their home. The largest
number of households (129 before Hurricane Florence and 133 at the time of the survey) made adisaster plan with their household members. In terms of other strategies, seven households
described actions taken either before or after Hurricane Florence, including limiting the amount offood kept in their freezer, taping their windows, stocking food and water, knowing where to go
should they need to evacuate again, and having a preparedness kit.
Table 3-14. Mitigation and preparedness strategies taken by households before Hurricane
Florence and at the time of the Wave 3c survey (after Hurricane Florence).
Strategies
Had before Hurricane
Have now
Florence
Yes
No
Don’t
Yes
No
Don’t
Know
Know
Elevate hot water heater and/or
32
253
12
45
215
10
HVAC
Elevate interior contents in
59
225
5
71
199
2
preparation for the flood
Assess the building (structurally) 25
233
29
33
210
22
by an engineer
Re-route ductwork from below
15
250
18
24
227
14
floor to attic space
Make disaster plan with
129
147
12
133
121
12
household members
Other
5
2
Households were then asked whether their involvement with their community (defined by the
respondent) and contacts with their neighbors or extended family had increased since Hurricane
Matthew. This set of questions reflect the construct of social capital and aim to show whethersocial capital and social connectedness have been affected by the disaster experience. Table 3-15
presents the responses to the social capital questions grouped based on tenure status. From the 192
homeowner households who responded, only 64 (33 %) believed their community involvement
has increased very similar to renters, 30 (33 %) of whom reported such increase. Changes wereslightly higher when households were asked about contact with their neighbors or extended family.

54

Of the 192 homeowner households, 72 (37 %) and of the 90 renter households 36 (40 %) reported
an increase in such contacts since Hurricane Florence
Table 3-15. Change in community involvement and contact with neighbors and extended family
since Hurricane Matthew.
Tenancy
Contact with
Increased sinceCommunity Involvement?
neighbors/extended
Hurricane Matthew
Count (%)
Family?
Count (%)

Owners(n=192)Renters(n=90)

Other
(n=6)

Yes

64 (33 %)

72 (38 %)No

120 (63 %)

108 (56 %)Don’t know

4 (2 %)

5 (3 %)

No answer

4 (2 %)

7 (4 %)Yes

30 (33 %)

36 (40 %)No

47 (52 %)

41 (46 %)Don’t know

1 (1 %)

2 (2 %)

No answer

12 (13 %)

11 (12 %)

Yes

-

-

No

3 (50 %)

3 (50 %)

Don’t know--

No answer

3 (50 %)

3 (50 %)

Household Socio-Demographics
The demographic characteristics of the households responding to the survey are shown in Tables
3-16 through 3-18. Respondents were asked to self-identify the race and ethnicity of theirhousehold. The values in Table 3-16 are similar to those for the City of Lumberton, but differ from
Robeson County, and the state of North Carolina, as reported in the ACS five-year estimates for2019 (ACS, 2020). For example, White (alone) is reported as 43 %, 28 %, and 71 % for the City,County, and State, respectively, compared to 31 % in the Wave 3c data.

55

Table 3-16. Race and ethnicity of surveyed households.
Count

%

White

110

31

Black or African American

130

37

American Indian or Native American

52

15

More than one race

13

4

Other

9

3

Hispanic or Latino

13

4Missing, Race

40

11

Missing, Ethnicity

47

13

Table 3-17 provides the maximum educational attainment of any individual residing in thehousehold. The values in Table 3-17 are similar in some cases and different in other cases whencompared to 2019 ACS five-year estimates. For example, the five-year ACS estimates for 2019
report 11 %, 20 %, and 19 % for the State, County, and City, respectively, for the percentage ofthe voting age population with less than high school education; all of which are much higher than
the 4 % of respondents surveyed. However, ACS reports 27 %, 34 %, and 32 % for the State,
County, and City, respectively, for the percentage of the voting age population with a highschool diploma, all of which are similar for the survey respondents in Wave 3c (28 %).
Table 3-17. Maximum household member education level of surveyed households.
Count

%

Less than high school

14

4High school

98

28

Associate’s degree or technical school

70

20

Bachelor’s degree

8023Master’s degree or higher

52

15

Missing40

11

Table 3-18 provides survey results for the respondent’s household’s combined annual income. The
values in Table 3-18 are similar to ACS data for Lumberton. For example, the ACS five-year
estimates for 2019 report 6 %, 14 %, and 14 % for the State, County, and City, respectively, forhouseholds earning less than $10 000 per year, compared to 14 % recorded in Wave 3c. Similarly,
56the ACS five-year estimates for 2019 report 12 %, 9 %, and 9 % for the State, County, and City,
respectively, for households earning between $75 000 and $99 999, compared to 6 % reported by
the respondents in Wave 3c. Overall, demographic representation of respondents in Wave 3c wasreasonably consistent with demographic information provided by 2019 ACS five-year estimatesfor North Carolina, Robeson County, and the city of Lumberton.
Table 3-18. Annual household income of surveyed households.
Count

%

Less than $10,000

50

14$10,000 to $19,999

63

18$20,000 to $29,99932

9$30,000 to $49,999

39

11$50,000 to $74,999

22

6$75,000 to $99,999

22

6$100,000 to $149,999

24

7More than $150,000

247Prefer not to answer

15

4Missing

63

18

Tracking longitudinal recovery
Throughout Chapters 2 and 3, longitudinal data has been presented. For example, in Chapter 2,
Figure 2-1 depicts the different flood extents in Lumberton comparing Hurricane Matthew and
Hurricane Florence aerial imagery. Also, in Chapter 2, Figure 2-5 depicts the spatial distributionof difference in damage state assessed for the housing sample after Hurricanes Matthew andFlorence. In this chapter, Tables 3-2 and 3-3 present the household-indicated damage after
Hurricanes Matthew and Florence; Table 3-8 presents changes in access after Hurricane Florencecompared to before Hurricane Matthew; Tables 3-10 through 3-12 present application, receipt, andtiming of receipt for a range of recovery resources made available after Hurricanes Matthew andFlorence; and lastly, Table 3-14 describes mitigation and preparedness actions taken before
Hurricane Florence and at the time of the Wave 3c survey.
This section presents one additional metric for tracking longitudinal recovery, namely reoccupancy. Here, re-occupancy is not reported using the survey questions (as is done withdislocation duration in Table 3-6 and Figure 3-3). Rather, this section presents re-occupancy basedon the completion codes filled out by the field study team before a survey was initiated. In thiscase, re-occupancy and abandonment are based on the field study team’s perceptions of occupancy
or abandonment where often this was confirmed by the surveyor with an occupant or contact with57

a neighbor. Three categories are presented in Figure 3-5: perceived or confirmed occupancy,
perceived or confirmed abandonment, and not assessed. The latter category varies based on fieldstudy team size and time in the field, where Wave 3a had the smallest team and shortest duration.
The decision to present these data is to provide information about the full sample of housing units
in each wave, as opposed to changing portions of the sample which completed surveys during eachwave.
As evident from Figure 3-5, the highest proportion of the housing sample was perceived to beoccupied during Wave 1, where all 861 housing units were assessed. In Wave 2, 13 of the units
were not assessed, 105 units that were perceived as occupied in Wave 1 were recorded asabandoned in Wave 2, whereas 45 of the housing units perceived as abandoned in Wave 1 wererecorded as occupied in Wave 2. Approximately six months later and shortly after Hurricane
Florence hit, 106 units that were recorded as occupied in Wave 2 were recorded as abandoned in
Wave 3a, whereas 32 units that were perceived as abandoned in Wave 2 were recorded as occupied
in Wave 3a. Wave 3a had the highest proportion (24 %) of the sample not assessed, which
consisted of 174 units recorded as occupied in Wave 2, 37 units recorded as abandoned in Wave
2, and 160 were recorded as occupied six months later in Wave 3c. At the time of Wave 3c, 640(74 %) of the sample was recorded as occupied, and 25 % were recorded as abandoned. Figure 36 provides a Venn Diagram showing which housing units were recorded as abandoned consistently
in one or more waves.
As shown in Figure 3-6, 12 housing units were recorded as abandoned in Wave 1 only, 26 in Wave
2 only, 52 in Wave 3a only, and 35 in Wave 3c only, whereas 29 housing units were recorded asabandoned in Waves 1, 2, 3a, and 3c. Wave 3a had the single most housing units recorded asabandoned, whereas there were 49 housing units consistently recorded as abandoned in Waves 2,
3a, and 3c. There are many reasons that could explain why housing units were not consistently
recorded as abandoned across waves, including variability based on which units were assessed by
the team during each wave, team members’ bias, household instability in the housing unit, damage
and new abandonment caused by Hurricane Florence, among other reasons. Both Figures 3-5 and3-6 communicate approximately 12 %, 18 %, 23 %, and 25 % of the housing sample were marked
as abandoned and 88 %, 81 %, 52 %, and 74 % of the housing sample were marked as occupied inWaves 1, 2, 3a, and 3c respectively. This finding implies a concerning upward trajectory ofabandonment across the longitudinal study nearly doubling from November 2016 to April 2019.

58

Figure 3-5. Sankey diagram depicting perceived or confirmed occupancy and abandonment of
housing sample across Waves 1, 2, 3a, and 3c.

Figure 3-6. Venn diagram with four sets depicting the number of housing units consistently
recorded as abandoned in one or more waves.

59

Chapter 4: Business Interruption and Recovery
4.1 Goals and Objectives
The business interruption and recovery component of the Wave 3c data collection was conductedin support of on-going research in the Center and at NIST on business functioning and operation,
including modeling efforts on business disruption and recovery, as well as potential links between
business and household recovery. The business component of Wave 3c was primarily built upon
the survey efforts of the Wave 2 business component of the Lumberton study.
The specific goals across the business interruption and recovery efforts in Wave 3c are to:● Collect data on continued recovery from Hurricane Matthew, thus extending the businesscomponent of the Lumberton study to a longitudinal data collection2● Document how long businesses were interrupted from both Hurricanes Matthew andFlorence and/or closed considering different initial damage levels, ownership structures,industry, available recovery resources, and accessibility issues● Better understand what interdependent infrastructure and services contributed to businessclosures and their reopening● Obtain business-level data on a range of factors expected to influence business interruption
and recovery, including recovery policy and finance programs
In the remainder of this chapter, we discuss how these goals were addressed in Wave 3c planning,
field data collection, and analysis in greater detail.

4.2 Sampling Procedure
The original business sample was developed for the Wave 2 data collection. Full details of theoriginal sampling strategy can be found in Sutley et al. (2021). In summary, the original (Wave 2)
business sample was drawn from the ReferenceUSA database (InfoGroup, 2016), provided by an
InfoGroup company of the same name for subscription by libraries, academic institutions or
government agencies to draw the business sample. Businesses were included if they were for-profit
organizations, had a physical location (rather than a post office box), and were “verified’ by
ReferenceUSA through phone calls to help reduce error in sampling operating establishments priorto Hurricane Matthew. Using ArcGIS, all businesses that fell within the Hurricane Matthew
inundation area—or a 100-m buffer around it—were included in the original sample (n = 218). An
additional random sample of businesses in the floodplain were identified, reaching a total sample
of 350 businesses, proportioned in a similar manner to the original housing sample.
Businesses from the initial sample were eligible for inclusion in Wave 3c data collection if theirlocation was sampled for the Wave 2 field study and there was still a physical structure at theaddress observed during Wave 3a. This includes businesses that completed the full in-person
survey in addition to businesses for which operating status was observed (without a completed

2

The first business survey took place during Wave 2 of the Lumberton study. Thus, the business survey that took
place during Wave 3C of the study was the first longitudinal follow-up for the business component.

60survey) during Wave 2. The number of observed and surveyed businesses eligible for inclusion in
Wave 3c was 229.
No new businesses were added to the sample as a result of Hurricane Florence. The locations
within Lumberton boundaries that were affected by Hurricane Florence and not by Hurricane
Matthew were primarily residential; researchers analyzed the original business population fromwhich the Wave 2 sample was created and found no businesses in the newly damaged area.
Therefore, the original business sample was maintained for Wave 3c.
The sector distribution of the sample based on North American Industry Classification System(NAICS) classification is provided in Table 4-1 below, in addition to a comparison of the overall
number of businesses in Lumberton based on the 2017 County Business Patterns and Economic
Census.

61

Table 4-1. Sector distribution of the sample.
Total Sample(Observed +Survey)Survey Only
SampleLumberton
Population
(all)

%

Count

%

Count

%

Count

22 Utilities

0%

1

1%

1

0%

3

23 Construction

5%

12

5%

6

9%

143

31-33 Manufacturing

4%

10

5%

5

4%

57

42 Wholesale

2%

5

2%

2

4%

62

44-45 Retail

34 %

77

44 %

49

19 %

286

48-49 Transportation/warehousing

0%

1

0%

0

4%

65

51 Information

2%

4

2%

2

1%

15

52 Finance/insurance

7%

15

5%

6

5%

71

53 Real estate/rental

4%

9

5%

5

3%

46

Professional, Scientific, and Technical
54 Services

6%

96

4%

8

1%

1

55 Management

0%

0

0%

0

0%

5

56 Administration

1%

3

1%

1

3%

51

61 Educational services

0%

1

0%

0

1%

8

62 Health care and social assistance

3%

6

1%

1

15 %

230

71 Leisure and Hospitality

0%

1

1%

1

1%

15

72 Accommodation and Food Services

19 %

43

14 %

15

10 %

150

81 Other services

13 %

29

14 %

16

14 %

213

NAICS
Code

NAICS Description

225

111

1516

As shown in Table 4-1, retail businesses, accommodation and food service businesses, and otherservice businesses continue to make up a large portion of the responding sample. However, this
sector distribution of those participating in the survey is similar to the larger Lumberton sample aswell as the overall business population in Lumberton.

62

4.3 Survey Instrument
The Wave 3a survey instrument was developed to mirror the Wave 2 business survey and the
Hurricane Matthew data collection as much as possible. The questions on the Wave 3c survey
retained the same wording as the previous survey when possible, in order to keep consistency
across measurements. As an additional way to evaluate respondent consistency between waves(and saliency across time), some questions were repeated verbatim in Wave 3c from Wave 2 inorder to see whether the business responded with the same answer. For example, businesses wereasked to re-assess their building, contents, and structural damage as a result of Hurricane Matthew
to compare to the damage state reported in the previous wave. If the business answered markedly
differently than it had previously, the researcher would then have the option to discard thatobservation as invalid. However, it is also possible for respondents to give a different answer due
to updated information or more complete information on their losses as time progressed.
The survey instrument was built around three primary objectives:
1. Understanding where the business was in recovery from Hurricane Matthew prior to the
Impact of Hurricane Florence. The first part of the survey asked the business to describe
where it was in its recovery from Hurricane Matthew prior to the Impact of Hurricane
Florence. The questions in the first section consisted primarily of those longitudinal
indicators of recovery. This includes a self-reported measure of recovery, questions on thebusiness’s profitability, and a question on the business’s perceived organizational capacity.
This section also included a question on how the business financed their recovery, which
resulted from the limited of financial assistance observed in the business community after
Wave 2.
2. Understanding how Hurricane Florence affected the business and how the business hasrecovered. This second part of the survey asked the business to recount their impact from
Hurricane Florence and assess their current recovery position and trajectory. This section
was almost identical to the Wave 2 business survey (Xiao et al., 2020),3 with the majordifference being that the business was asked to respond about Hurricane Florence, rather
than Hurricane Matthew. Since Hurricane Florence had more wind effects than did
Hurricane Matthew, which was corroborated by business observations during Wave 3a, thedecision was made to add damage state information and related questions for wind damage
in addition to flood water damage to the business survey. Definitions for the wind-driven
damage state was added to the damage state handout given to the business (see Appendix
2B). Questions on business characteristics, for example ownership structure and whether
3

For full survey, see Sutley, E., Dillard, M. and van, J. (2021), Community Resilience-Focused
Technical Investigation of the 2016 Lumberton, North Carolina Flood: Community Recovery
One Year Later, Special Publication (NIST SP), National Institute of Standards and Technology,
Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.SP.1230-2,
https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930866 (Accessed June 16, 2021)

63the business rented the building, were kept in the survey in case the business inhabiting thesampled structure had changed within the previous year and a half (since wave 2).
3. Understanding how the business has mitigated, adapted, and prepared as a result of both
events. The third major survey section aimed to understand what kinds of mitigation,
adaptation, and preparedness actions the business took ahead of and resulting from
Hurricane Matthew and Florence. Due to the potential constraints faced by renters in doing
any physical modifications of the structure, questions were separated by ownership statusof the building. Questions were modified from the 2009 business survey in Galveston, TX
after Hurricane Ike from which the original survey was also adapted (Xiao & Peacock,2014). Businesses were asked whether they floodproofed the building, secured a secondary
storage location, performed risk assessments, developed emergency plans, and otheranticipatory actions. For each action, the business was asked whether the measure wastaken prior to Hurricane Matthew, between Hurricane Matthew and Hurricane Florence,
after Hurricane Florence, or not at all. These questions can inform both how repeat eventsaffect adaptive behavior, as well as whether these measures were effective in reducing
damage and event-related losses.
4. Understanding Operator Demographic Characteristics and Business Characteristics.
Questions on social and demographic characteristics of the respondent were duplicatedfrom the Wave 2 business survey and included categorizing respondents as renters orowners of their business property, race, ethnicity, level of education, and year of experience
as a business manager or owner. These types of questions and the associated data document
whether our sample matched the distribution of socio-demographics for Lumberton as awhole, and help us determine if the same business representative had been surveyed across
Waves 2 and 3.
Researchers expressed the major objectives of each section of questioning when surveying a
respondent in the field. The goal was to reduce error in those surveyed engaging in retroactive
sensemaking – trying to make sense of their experiences after the fact when engaging in thesurvey process. Furthermore, there was an effort to make the instrument brief, while maintaining
the scope described in the research objectives. Given the necessity of differentiating between theeffects of both hurricanes and compounded recovery challenges, the final survey length was four
pages, front and back. The final page of the survey asked the respondent to provide theirdemographic information, general information on the business, and to add any comments thebusiness had in conclusion. The Wave 3c business survey is provided in Appendix 4A. The
business surveys were conducted face-to-face by interviewer teams of two. An information sheet
about the field study and Center project were handed to potential respondents; a consent script
was used to obtain verbal consent prior to surveying (see Appendix 4B for the information sheet
and Appendix 3C for the consent script).

644.4 Data Collection Methodology
4.4.1. Field Procedures
Researchers were split into field teams of two individuals, when possible. Mirroring the processused for the household survey, a Google map of business locations was updated daily and used by
the research teams in the field on their mobile devices. During Wave 3c, the Google map was
updated live by researchers in the field to help coordinate across teams.
During Wave 2, researchers in the field encountered some issues with business respondent
availability; this information was incorporated into the Wave 3c procedures. For example, Wave
2 respondents were unavailable when businesses were too busy with customers, the business hours
were limited, and/or the owner/manager was away on travel or not in the office for another reason.
To address this issue in Wave 3c, names and email addresses were collected from respondents
allowing business managers/owners to take the survey online at their convenience in future waves,and reduce the burden on researchers in the field, though an online option was not provided forWave 3c.
Similar availability issues occurred during Wave 3c. In some cases, respondents or their coworkers suggested that the researcher return at a different time. Given the limited time andpersonnel in the field, the researcher prioritized businesses for a revisit depending on their
responses in Wave 2. Businesses unaffected by Hurricane Matthew, and businesses that provided
limited responses in Wave 2, were assigned the lowest priority. The success rate for revisits varied,
but the researchers were able to either collect email addresses or provide information for how the
respondent could take the survey online at their leisure. In some cases, a copy of the survey wasleft behind for respondents to complete and to be picked up by a field team member at a time
specified by the respondent. After Wave 3c concluded, the researchers attempted to follow up withrespondents that had provided email addresses or phone numbers with limited success. The
outcome further emphasized the importance of in-person interviews for such surveys.
In some cases, it was noted during Wave 3c that the business at the sampled locations had changed
since Wave 2. This issue may have been related to medium- or long-term impacts of the two
Hurricanes but occurred between the two events. In cases where there was a new/different businessat a previously surveyed location, researchers attempted to verify when the previous businessclosed with the owner or manager of the new business in the same location. In a few cases, theowner or manager of the new business was able to provide the updated location of the previously
surveyed business from Wave 2. Public records were checked to determine the date that thebusiness moved. Follow-up contact was attempted, but in most cases was unsuccessful, especially
if the business had left Lumberton. There were challenges involved in verifying the situation whenbusinesses had closed or moved when there was not a new business occupying the building unit.
Where possible, researchers attempted to obtain information from neighboring businesses.
Additional closure verification was done after the researchers returned from the field, which is
discussed in Section 4.2.
Other forms of attrition included businesses that were fatigued by continued participation in thesurvey; i.e., the respondent felt that they had contributed during Wave 2, but did not appreciate aneed to answer similar questions a second time. Yet, only three businesses refused to respond65

citing survey fatigue as their reason. Some businesses were hesitant to respond to the survey
without permission from their corporate office. However, there were also many businesses that
remembered responding to the Wave 2 survey and were enthusiastic to contribute to thelongitudinal study by participating in the Wave 3c survey. The survey was only offered in English.
There were three occasions when business owners or managers were interested in participating,but they were preempted from doing so due to a language barrier.
A major challenge for the Wave 3c survey effort was differentiating between businesses’
experience of the two Hurricanes and appropriately disentangling impacts. This was a challenge
for both the researchers in the field and for survey respondents. To reduce confusion, a briefsummary sentence was written for each survey section, which the surveyor would read to the
respondent. Highlights and font color were used strategically in the survey instrument to help withclarification. The summary text for each section was highlighted for emphasis. Directional text,
such as clarification for challenging questions or when the business needed to refer to additional
material, was written in red.
4.4.2. Data Entry and Cleaning
The method of data entry and data cleaning was similar to that of Wave 2 with some modifications
reflecting the lessons learned during previous field work. For data entry the team used Qualtrics,
and for data cleaning STATA was used. Cleaning and entry for the business survey and the housing
survey were conducted in close conjunction using similar codes for both surveys. Changes fromthe Wave 2 process to help improve data quality in Wave 3c included making data cleaning
decisions in the field and prior to data entry (e.g., taking the higher value of a range whenprovided), using STATA as the primary data cleaning software, and using STATA’s codebook
command to generate the codebook of variables and record the survey metadata.
In Wave 2 the data cleaning process for the housing and business data collections differed. In
Wave 3c, the team coded data for the business survey in a manner more consistent with the housing
survey. There are some survey questions that accept a range, as opposed to a point value, especiallywhen taking notes in the field and accounting for uncertainty expressed by respondents. The fieldteam decided to take the highest value when a range of values was provided by the respondent(and noted by the surveyor). This differed from Wave 2, for which data were cleaned using the
midpoint value of the range. In light of this decision, Wave 2 business data were re-cleaned toensure that there was consistency with Wave 3c data. It should be noted that when runningsummary statistics on the affected variables, taking the midpoint versus the high point made no
noticeable effect.
Similar to the procedure employed following Wave 2, researchers verified all field-reported
closures and business relocations through online record searches. Online sources included tax
registration searches, google imagery, the business’s previous online or social media presence, andthird-party business sites such as Yelp. Multiple sources were triangulated and viewed as a whole
to make the final decision on the business’ current operating status. Given that the unit of analysis
is the physical structure, data was collected from the current business building occupant when abusiness had moved or gone out of business.

664.5 Results
In total, 111 businesses responded to the survey. The team also gathered observational informationfor 225 businesses (including the 111 businesses referenced previously), documenting whether thebusiness was open, closed, or moved. In cases of a business that moved location, this wasdetermined initially via internet information and confirmed by surveyors in-person. The Wave3cof data collection also helped clarify some of the conclusions from Wave 2 and provide updated
information on error in the original database. As discussed in Section 4.2, the final sample at Wave
2 included observational data for 229 businesses. Of these businesses, after talking to neighboringbusinesses during Wave 3c and having another year for online records to update, 11 businesses
were identified to be nonprofits, rather than for-profit businesses, and deemed to be ineligible.
Others that were not able to participate in Wave 3c included those that had closed since or prior to
Hurricane Matthew. One business was interviewed at a location other than its business location,
but it was ultimately left in the sample. This left 219 commercial units in the sample. There were
six new businesses that began to occupy these structures after Hurricane Matthew that were added,
leading to the final 225 sample. Counting all businesses that were in the Wave 3c sample, which
includes closed businesses, the survey response rate was 49 %.
The operating status for the 225-business sample is provided in Table 4-2, below.
Table 4-2. Operating status of the business sample.
Operating Status

Count

%

Open

165

73 %

Closed

43

19 %

Moved

17

8%

Total

225

100 %

Of the businesses visited during Wave 3c, 73 % were open, 19 % were closed, and 8 % had moved
to a new location, either within or outside Lumberton city limits. It should be noted that the original
sample consisted of an over-sampling of businesses that were more likely to be damaged,
specifically those that were located in the projected inundation area from Hurricane Matthew andin the 100-year floodplain. Therefore, this is not an accurate representation of all Lumberton
businesses since this table, and all tables in the chapter are not weighted based on their sampling
probability. The results in this section are presented aligning the three major sections of thebusiness survey, as outlined in Section 4.3: (1) understanding the business level of recovery from
Hurricane Matthew prior to the impacts attributable to Hurricane Florence, (2) understanding how
Hurricane Florence affected the business and how the business has recovered, and (3)understanding how the business has mitigated, adapted, and prepared as a result of both events.
4.5.1 Hurricane Matthew Recovery Follow-up
The survey asked about businesses’ operating status immediately before Hurricane Florence, as
noted in Table 4-3. Only 1 % of respondents reported that they were still operational, but expect
to never recover fully and 4 % were still in survival or response mode from the impacts of67

Hurricane Matthew. Of those still in the recovery stage, 13 % are recovering and an additional 13% are mostly recovered. Finally, 68 % of respondents reported being fully recovered from
Hurricane Matthew immediately before Hurricane Florence.

Table 4-3. Recovery status from Hurricane Matthew immediately before Hurricane Florence.
Recovery Status Prior to Hurricane Matthew
Count%
1. Still in survival/response mode
44%
2. Recovering
13
13 %3. Mostly recovered
13
13 %4. Fully recovered
67
68 %5. Still operational but will never recover
1
1%
Total
98
100 %
In Wave 3c, we also followed up on how respondents financed their business’ recovery from
Hurricane Matthew. These results are reported in Table 4-4 for those who responded to thequestion. Respondents were able to select one or more source of finance. Personal savings wereused most frequently (48 %) to finance at least part of recovery. Corporate assistance was used by
15 % of respondents and 20 % employed insurance in their recovery. Notably, the use of donations(<1 %), assistance from friend or family (3 %), and Federal assistance programs (3 %) were allvery low.
Table 4-4. Self-reported financing sources used for recovery from the impacts of Hurricane
Matthew (n=49).
Finance Sources used in Recovery from
Hurricane Matthew (% of Total Recovery
Mean Std. Dev.
Min
Max
Finance)
Count
(%)
(%)
(%)
(%)
0%
Personal savings
49
48 %
49 %
100 %
0
%
Credit card
49
<1 %
1%
10 %
Corporate assistance (or assistance fromanother branch or location)
49
15 %
35 %
0%
100 %
0%
Insurance
49
20 %
36 %
100 %
0%
Donations
49
<1 %
3%
20 %
0%
Crowd funding
49
0%
<1 %
0%
0
%
Assistance from friends or family
49
3%
16 %
100 %
0%
Federal assistance programs
49
3%
15 %
100 %
0%
0%
State assistance programs
49
0%
0%
0%
0%
Local assistance programs
49
0%
0%
0
%
Other
49
10 %
26 %
100 %

68

Table 4-5a provides the breakdown of respondents by whether additional repairs to their structure
were needed from the physical impacts of Hurricane Matthew immediately before Hurricane
Florence. Repairs were still outstanding for 17 % of respondents while 58 % had already made allnecessary repairs. Finally, 24 % of respondents did not have physical damage from Hurricane
Matthew that required repair. Of those businesses that still required repairs, the percent ofremaining repairs reported (n=14) are shown in Table 4-5b.
Table 4-5a. Businesses that still required repairs from Hurricane Matthew impacts immediately
before Hurricane Florence.
Business still requires repairs from
Hurricane Matthew
Count
%
Yes
18
17 %
No
60
58 %
N/A (not affected by hurricane)
25
24 %
Total
103
100 %
Table 4-5b. Percent of remaining repairs still need to be completed for those that still require
some repairs from Hurricane Matthew pre-Hurricane Florence.
Remaining
repairs (%)
Count
%
5
1
7.14
20
2
14.29
25
1
7.14
30
1
7.14
35
1
7.14
40
2
14.29
50
1
7.14
75
2
14.29
100
3
21.43
Total
14
100.00The extent to which the impacts of Hurricane Florence affected the physical recovery of businesses
from Hurricane Matthew is described in Table 4-6. Of all respondents, 42 % reported no impact
on ongoing Hurricane Matthew repair from Hurricane Florence. This could be from those alreadyfully repaired from Hurricane Matthew or that remaining damage from Hurricane Matthew thatcan be disentangled from damage directly by Hurricane Florence. There were some businesses
damaged by Hurricane Matthew that suffered additional damage from Florence – a little (9 %),moderately (8 %), and severely (14 %). Twenty-seven percent reported N/A, indicating that theyexperienced no impact to their structure from Hurricane Florence.

69

Table 4-6. Extent to which Hurricane Florence affected ongoing business recovery from
Hurricane Matthew.
Extent of the Effect of Hurricane Florenceon Hurricane Matthew Recovery
Count%
Not at all42
42 %A little
99%Moderately
8
8%
Severely14
14 %
N/A (not affected by the Hurricane)
27
27 %
Total
100
100 %
4.5.2 Hurricane Florence Damage and Interruption
Businesses in the sample varied in their impact from Hurricane Florence. Table 4-7 displays thedifferent types of damage the businesses experienced to their building (due to both flood and wind),their contents, and their machinery.
Table 4-7. Damage states reported by the businesses resulting from Hurricane Florence.
Damagefrom
Hurricane
Florence Building (flood)
Building (wind)Contents
Machinery
Count
%
Count
%
Count
%
Count
%
None

87

79 %

74

70 %

74

69 %

88

84
%

Minor

14

13 %

18

17 %

15

14 %

8

8%

Moderate

4

4%

12

11 %

11

10 %

2

2%

Severe

5

5%

5

5%

4

4%

5

5%

Complete

0

0%

0

0%

3

3%

2

2%

Total

110

100 %

109

100 %

107

100 %

105

100
%

The majority of businesses reported no or minor damage from Hurricane Florence. Yet, somebusinesses did report some flood damage attributable to Hurricane Florence: 21 % experienced
some level of damage to their flood damage to the building, 30 % experienced wind damage to thebuilding structure, 31 % experienced damage to the business’ contents, and 16 % reported
machinery damage. It should be noted machinery damage is not a category of potential damage
that is relevant across business types depending on their sector and/or set-up of their physical work
location.

70

In all categories, “minor” was the most frequently reported damage level among businesses thatexperienced any damage. Eleven percent of businesses surveyed experienced moderate winddamage and 10 % experienced moderate damage to contents, but very few experienced severe orcomplete damage in any category.
Businesses can experience losses as a result of utility disruption in addition to physical damages.
The percentage of businesses that lost critical utilities from Hurricane Florence and the average
duration of that loss is presented in Table 4-8. Eighty percent of businesses reported losing
electricity after Hurricane Florence, which was the most frequently reported utility loss. Internet/IT
was the second most common utility loss (45 % reporting losing service) followed by landline (39%). Internet/IT, landline, and electricity are all closely related, which could explain why they
consist of the three most common losses. These three services were also disrupted for the longest
time. Electricity was out for businesses for an average of 5 days, internet/IT was out for an averageof 4 days, and landline phone service was out for an average of 3 days. Water was both the fourthmost reported utility disruption and the fourth longest duration of loss with 27 % of businesses
reporting losing water for an average of 3 days. Sewer, cell, and natural gas service disruptions
were experienced by fewer than 15 % of businesses and for relatively short durations.
Table 4-8. Percent of businesses reporting utility loss and the number of days of disruption
resulting from Hurricane Florence.
Utility Disruption% lostAvg. days lost

Std. Dev.

Electric

80 %

5

5

Water

27 %

3

6

Natural Gas

6%

2

13

Sewer

14 %

2

7

Landline

39 %

3

7

Cell

14 %

1

23

Internet/IT

45 %

4

7

The Wave 3c survey asked about physical accessibility issues (e.g., closure of roads or sidewalks)
after Hurricane Florence. Of the 108 respondents to this question, 65 % experienced suchaccessibility issues, which could limit their ability to access the site to assess damage and start
repairs and also could limit customers from coming to the business, impacting revenue andrecovery. Similarly, respondents were asked if any of their employees had issues coming to work(at that location) with more than one option that could be selected for this question. Response
frequencies for this question are presented in Table 4-9. Employees were most frequently unable
to report to work due to transportation issues after Hurricane Florence (63 %), personal homedamage (40 %), and issues related to children not able to return to school (29 %). Physical and71mental health issues were rarely cited as reasons for not reporting to work, though these factors
may be less likely to be reported to the owner or manager of the business.

Table 4-9. Self-reported issues for employees trying to get to the business following Hurricane
Florence.
Employee IssuesCount

Yes (%)

No (%)

Transportation problems

104

63 %

37 %

Need to repair their home

101

40 %

60 %

Children were not back in school

100

29 %

71 %

Physical health issues

101

0%

100 %

Mental health issues

100

2%

98 %

Respondents were asked to indicate whether they had lost customers following Hurricane Florence
without specifying a timeframe in this question. Of those who responded to this question (n=103),
50 % experienced loss of customers to some extent relative to pre-Hurricane Florence. Those wholost customers were asked to estimate the percent of customers lost and the mean value reported
was 12 % (S.D. 19 %).
Both damage and utility disruption can lead to business interruption and reduced business capacity.
In the survey, interruption is defined as the number of days the business was closed to the public.
Capacity is defined as aspects of the business that are most important to—and defined by—the
respondent, for example the quality and/or quantity of service or product offerings. Figures 4-1
and 4-2 show days of interruption for the responding businesses and the capacity of the businessafter Florence. As shown in Figure 4-1, the average period of business interruption from Hurricane
Florence was 10 days (S.D. 15).

72

Number of Businesses

70
60
50
40
30
20
10
0
0

1-7
8 - 1415 - 21 22 - 28 29 - 35 36 - 42 43 - 49
Number of Days Needed for Businesses to Resume Operations

50+

Figure 4-1. Number of days until businesses to resumed operations after Hurricane Florence (inseven-day increments).

70

Number of Businesses

60
50
40
30
20
10
171 - 180

161 - 170

151 - 160

141 - 150

131 - 140

121 - 130

111 - 120

100 - 110

91 - 100

81 - 90

71 - 80

61 - 70

51 - 60

41 - 50

31 - 40

21 - 30

11 - 20

0 - 100

Current Operational Capacity (%)

Figure 4-2. Return of business capacity after Hurricane Florence (in 10 % increments), reported
at the time of Wave 3c data collection.
As shown in Figure 4-2, the majority of businesses were interrupted within the first week postHurricane Florence, with the number of businesses interrupted declining in subsequent weeks. A
large majority of businesses appear to have been interrupted for less than one month and the longest
reported interruption is approximately three months or 90 days. With respect to capacity, thelargest number of businesses reported being at 100 % capacity. Most of the sample falls within the73range of 50 % to 100 % capacity, but a small number of businesses reported being over 100 percent
capacity4, and some reported being at below 50 % capacity.
To better understand businesses’ decision making, we asked about the influence of decisions madeby businesses on the same street block regarding the decision the respondent made for theirbusiness to remain open or to close (temporarily). Responses to this question are provided in Table
4-10. Only 4 % indicate that decisions of other businesses are neither important nor unimportant.5
The majority of respondents (57 %) indicate that they assign no importance at all of the decisionsof surrounding businesses, while 38 % note that the decisions of surrounding business are
somewhat or very important to their own decision to open or close their business.
Table 4-10. Importance of decisions of surrounding businesses (those on the same block) to thedecision for the business to remain open or to close.
Importance of decisions of surrounding businesses Count%
Not important at all
59
57 %Somewhat unimportant11
11 %Neither important nor unimportant
4
4%
Somewhat important17
17 %Very important
12
12 %
Total
103
100 %
4.5.3 Hurricane Florence Recovery
With respect to recovery, the survey asked businesses about changes to their profitability as a resultof Hurricane Florence in addition to their self-reported recovery status. Table 4-11 displays thereported changes in profitability and Table 4-12 shows self-reported recovery statuses.
Table 4-11. Business profitability change as a result of Hurricane Florence.
Revenue Change after Hurricane Florence
Count%
Greatly increased22%Increased
6
6%
No change
76
78%
Decreased
10
10%Greatly decreased
4
4%
Total
98
100%

4

There is not a clear explanation as to why this may be the case, as these businesses do notgroup into a particular sector or ownership structure.
5
It is very possible that the traditionally neutral category in this Likert scale (neither important
nor unimportant) might be more conceptually similar to “somewhat unimportant” or “notimportant at all.” Further analysis will need to examine this coding more closely.
74

Table 4-12. Business self-reported recovery status after Hurricane Florence.
Recovery Status after Hurricane Florence Count%
Still in survival/response mode
5
5%
Recovering
18
17%
Mostly recovered
22
21%
Fully recovered
58
56%
Still in operation but will never recover
0
0%
Total
103
100%
Looking at Table 4-11, a majority of businesses (78 %) reported no change in revenue as a resultof Hurricane Florence. Decreased revenue was the second-highest reported category (10 % of the
responding sample). In other categories, 6 % of businesses reported an increase in revenue, 4 %reported greatly decreased revenue, while 2 % had a greatly increased revenue. When asked toreport their own perception of their recovery, over half of businesses (58 %) reported being fully
recovered at the time of wave 3c relative to their state ahead of Hurricane Florence. As shown in
Table 4-12, very few businesses reported that they would never recover or were still insurvival/response mode (5 %), with the remaining businesses reporting being mostly recovered(22 %) or recovering (18 %). Again, as discussed in Section 4.5, these statistics reflect the raw
responses that have not been weighted with respect to their probability of being damaged.
Oversampling businesses in the inundation area might cause a higher percentage of businesses thatare still recovering or are not as far along in their recovery process.
Respondents were asked whether the location of their suppliers has changed post-Hurricane
Florence compared to the period between Hurricane Matthew and Hurricane Florence.
Specifically, the question asked for agreement with the statement “we have more suppliers outside
our city than we did prior to Hurricane Florence (Between Hurricane Matthew and Hurricane
Florence).” Of those who responded (n=101), 20 % disagree or strongly disagree with thestatement, 71 % find it to be a neutral statement, and only 9 % agree or strongly agree indicatingthat for most businesses the number of outside suppliers has gone down.
Insurance is a means by which businesses plan ahead of an event to help hedge the risk of impacts.
We asked about insurance coverage before Hurricane Florence and outcomes of insurance claims
post-Hurricane Florence. The percent of insurance holders is shown in Table 4-13. The greatest
frequency of insurance type reported is for building insurance (71 %) and content insurance (29%). Additionally, some respondents held interruption insurance (37 %) and a significant portion
had liability insurance (66 %).
We further asked if the business had made an insurance claim, presenting the results in Table 413. Of those who responded, 38 % filed an insurance claim for building damage, 52 % of whom
received a payout on average 90 days after filing (S.D. 57 days). Thirty percent of respondents
made a claim on content insurance, 50 % of whom received the payout within an average of 66
days months (S.D. 33 days). Claims against interruption and liability insurance were lessfrequently reported, with 11 % and 7 % reporting making such a claim, respectively.

75

Table 4-13. Insurance coverage, claim receipt, and timing after Hurricane Florence.
Building(structure)
Insurance n=77

Content
Insurance n=75

Interruption
Insurance n=68

Liability
Insurance n=73

Count (%)

Count (%)

Count (%)

Count (%)

Had coverage (n=77)

54 (71 %)

51 (29 %)

25 (37 %)48 (66 %)Filed claim

18 (38 %)

13 (30 %)

4 (11 %)

3 (7%)

Received payout

11 (52 %)

9 (50 %)

1 (10 %)

1 (10 %)

Average days toreceive payout

90

66

60

-

Std. dev. days toreceive payout

57

33

0

-

Insurance

Table 4-14 presents the proportion of businesses surveyed who applied for different types ofassistance and those who reported receiving such assistance. The rates of application reported arevery low, with 3 % of respondents requesting FEMA financial assistance and 1 % requesting an
SBA loan. There were no reported use or requests for other federal or state funds nor private bankloans. Additionally, there was no reported requests made for or use of sources such as faith-basedorganizations or non-governmental organizations (NGOs). Additionally, no use of fundraisers orcrowdfunding was reported. In general, as evident from Table 4-14, a significant proportion of
impacted businesses do not apply for external recovery funding in Lumberton. The few who didrequest such assistance did not receive it.
We asked respondents to consider how many days their business can function in a deficit. Those
who responded to the question (n=78) noted an average of 256 days with standard deviation of
480 days.

76

Table 4-14. Assistance requested and received by businesses.
Yes

No

Assistance

Total

Count (%)

Count (%)

FEMA financial assistance67

2 (3 %)

65 (97 %)SBA (Small Business
Administration) loan67

1 (1 %)

66 (99 %)Other federal or state funds

65

0 (0 %)

65 (100 %)Local government funds

65

0 (0 %)

65 (100 %)Financial assistance fromany church or othernon-governmentorganizations

65

0 (0 %)

65 (100 %)Clean up or repair help
from church or othernon-governmentorganizations

65

0 (0 %)

65 (100 %)Loan from a Bridge Loan
program65

0 (0 %)

65 (100 %)Private bank loans

65

0 (0 %)

65 (100 %)Crowdfunding65

0 (0 %)

65 (100 %)Fundraisers65

0 (0 %)

65 (100 %)

Other

63

1 (2 %)

62 (98 %)4.5.4 Business Information and Respondent Details
Table 4-15 provides the federal classification of surveyed businesses. The majority (79 %) haveno official federal classification. Of those with such classifications, 4 % are women-owned, 6 %are minority certified by the SBA (8a), and 3 % are veteran-owned.

77

Table 4-15. Businesses’ self-reported federal classifications.
Federal Classification

Count

%

Woman-owned

44%Minority certified by the SBA (8a)66%Veteran-owned

3

3%

None

82

79 %

Do not know

55%

Did not ask

4

4%

Total

104

100 %

Data on the business and its location were obtained in the survey. For those who responded (n=101)
the average length of time at the location was 20 years (S.D. 16 yr.). The ownership of the businesslocation/structure directly influences the type of preparedness and adaptation actions available tothe business. Of those responding (n=108), there was an even split between those who own theirlocation (49 %) and those who rent their business location (49 %), with 2 % reporting “other.”
Ownership structure of a business has been shown to influence preparedness levels (Barman et al,2012), but this finding is inconsistent across the literature (e.g., Webb et al., 2000). Respondents(n=108) indicated their ownership structure ahead of Hurricane Florence shown in Table 4-16. The
majority identify their business as part of a corporation or franchise (46 %), single owner (40 %),partnership (12 %), and other (2 %).
Table 4-16. Businesses’ self-reported ownership structure ahead of Hurricane Florence.
Ownership Structure

Count

%

Single owner

43

40 %

Partnership

13

12 %

Corporation or franchise

50

46 %

Other

2

2%

Total

108

100 %

The Wave 3c survey captured respondents’ role with the business for which they responded. Of
the 112 business responses to this question, 22 % are business owners, 61 % are managers, 16 %have the dual role of owner and manager, and one respondent held another role (i.e., senior
employee). For those who responded (n=107), the average length a respondent had been a businessowner or manager for any business was reported as 18 years (S.D. 14 yr.).
78

The Wave 3c survey repeated additional questions related to respondents’ demographic
information. We obtained data from 103 respondents on their age with the average reported age at
49 years (S.D. 14 yr.). The average number of years of schooling reported was 14 (S.D. 2 yr.)(n=105).
Only 6 % of respondents (total n=106) identify as Hispanic. Self-reported race and ethnicity of
respondents is reported in Table 4-17. The majority of respondents identify as white, while 15 %identify as Black or African American, and 14 % identify as American Indian or Alaska Native.
Table 4-17. Race and ethnicity of surveyed business owners/managers.
Race of Owner or Manager

Count

%

White

65

62 %

Black or African American

16

15 %

American Indian or Native American

15

14 %

Asian

1

1%

Native Hawaiian or Pacific Islander

11%More than one race

5

5%

Other

1

1%

Hispanic or Latino

6

6%

Total

106

100 %

4.5.5 Longitudinal Trends
Mitigation and preparedness actions can help businesses escape severe disaster impact andimprove recovery outcomes. Business respondents were asked a series of questions concerning
their mitigation and preparedness actions based on whether they owned the premises of thebusiness and whether the actions were taken before Hurricane Matthew, between Hurricane
Matthew and Hurricane Florence, or after Hurricane Florence. Table 4-18 shows results forbusinesses that owned their premises and could make changes to the structure itself.

79

Table 4-18. Mitigation, adaptation, and preparedness measures for respondents that own thebusiness premises/storefront.
SecuredSecondary
Storage
Location

Had the
Building
Structurally
Assessed

Floodproofing
of building(professional)

Floodproofing ofbuilding (nonprofessional)

Count

%

Count

%

Count

%

Count

%

Adopted before
Hurricane
Matthew

1

1%

9

10 %

9

10 %

9

11 %

Adopted between
Hurricane
Matthew and
Hurricane Florence

2

2%

12

14 %

12

14 %

5

6%

Adopted after
Hurricane Florence

2

2%

1

1%

0

0

3

4%

Adopted (any)

5

6%

22

25 %

21

24 %

17

20 %

No adoption

81

94 %

65

75 %

67

76 %

66

80 %

Total

86

100 %

87

100 %

88

100 %

83

100 %

HM= Hurricane Matthew, HF= Hurricane Florence
As shown in Table 4-18, most respondents did not take any of the mitigation actions listed that are
relevant to those who own their business location. These options included: floodproofing thebuilding themselves or through a professional, securing a secondary storage location, or having
the building structurally assessed. Professional floodproofing was the most reported action thatbusinesses took, followed closely by securing a secondary storage location. Approximately aquarter of businesses responding to each question had taken those actions at some point prior to
the Wave 3 survey. Looking at the timing of these actions, it appears many businesses thatmitigated did so either before Hurricane Matthew or between Hurricane Matthew and Hurricane
Florence.
Table 4-19 presents mitigation and preparedness actions that businesses may have taken,
regardless of whether they owned the building in which they conduct business. These options
include: performing a risk assessment, adopting strategies to stay informed of weather warnings,
developing an emergency plan or checklist, assigning disaster responsibilities to certain
employees, and performing emergency drills. Forty-eight percent of businesses reported adopting
strategies to stay informed of weather warnings and 47 % have an emergency plan. Assigningdisaster responsibilities to certain employees was also a commonly adopted measure with 34 % ofbusinesses responding that they had taken this action in any time period. Adoption was lowest forrisk assessments and emergency drills, with 17 % of businesses reporting having taken these80measures. For most of the mitigation actions they adopted, businesses had these already in placeprior to Hurricane Matthew.
Table 4-19. Mitigation, adaptation, and preparedness measures for all respondents.

Performed a
Risk
Assessment

AdoptedStrategies to
Stay Informedof Weather
Warnings

Developed an
Emergency
Plan or
Checklist

Assigned
Disaster
Responsibilities
to Certain
Employees

Performed
Emergency
Drills

Count

%

Count

%

Count

%

Count

%

Count

%

Adoptedbefore
HM

10

11 %

31

32 %

30

30 %

27

28 %

10

10 %

AdoptedbetweenHM and
HF

4

4%

13

13 %

15

15 %

4

4%

4

4%

Adoptedafter HF

2

2%

3

3%

2

2%

2

2%

3

3%

Adopted
(any)

16

17 %

47

48 %

47

47 %

33

34 %

17

17 %

No
adoption

76

83 %

50

52 %

54

53 %

64

66 %

82

83 %

Total

92

100 %

97

100 %

101

100 %

97

100 %

99

100 %

HM= Hurricane Matthew, HF= Hurricane Florence
The following figures illustrate observed trends based on longitudinal responses for multiple time
periods: before Hurricane Matthew, after Hurricane Matthew, before Hurricane Florence, and after
Hurricane Florence. Questions that relate to before and after Hurricane Matthew originate fromthe Wave 2 survey, which asked businesses to consider the time immediately before Hurricane
Matthew (October 2016) and the time of the survey which was 18 months post event (January2018). Questions that relate to before and after Hurricane Florence originate from the Wave 3csurvey, which asked businesses to consider the time immediately before Hurricane Florence(September 2018) and the time of the survey (April 2019).
Figure 4-3. illustrates business operational capacity, based on 72 longitudinal responses covering
the three time periods. On average, respondents report being at about 95 % capacity after Hurricane
Matthew and before Hurricane Florence. Following Hurricane Florence, business capacity drops
to about 92 %, on average. Note that some businesses report capacity over 100 %, which is why
the error bars (especially after Hurricane Florence) are so wide.

81

Figure 4-3. Business operational capacity: after Matthew, before Florence, and after Florence.
Figure 4-4 demonstrates the change in number of employees, based on 60 longitudinal responses
covering the three time periods. The impact of Hurricane Matthew on employment on average isa less than 1 % decrease, with 25 % of respondents reporting at least 5 % decrease. Prior to
Hurricane Florence, many more respondents reported positive change in employment relative tothe post-Matthew time period. On average, respondents reported roughly 15 % increase inemployment, with over a quarter of respondents reporting 30 % increase or more.

82

Figure 4-4. Average number of employees reported: before Matthew, between Matthew andFlorence, and after Florence.
Figure 4-5 illustrates respondents’ self-reported recovery status, based on 90 longitudinal
responses covering the three time periods. After Hurricane Matthew, most respondents reported
being mostly recovered, with responses ranging between recovering and fully recovered.
Immediately before Hurricane Florence, most respondents reported being somewhere between
mostly recovered and fully recovered. After Florence, this drops back down to mostly recovered.

83

Figure 4-5. Recovery status after Matthew, between Matthew and Florence, and after Florence.
Figure 4-6. demonstrates self-reported business profitability, based on 97 longitudinal responses
covering the three time periods. After Matthew, most businesses reported being somewhere
between profitable and highly profitable. Immediately before Florence, this falls to somewhere
between breaking even and profitable, suggesting other trends and business concerns impact
business profitability. After Florence, responses are largely unchanged.

84

Figure 4-6. Business profitability: before Matthew, between Matthew and Florence, and after
Florence.

85

Chapter 5: Conclusions and Next Steps
5.1 Conclusions for Damage Measurement following Successive Flood Events
The community of Lumberton experienced two extreme hurricane-induced floods over a span of
only two years (2016-2018). Based on USGS stream gage data, the intensity of flooding imposedon Lumberton by Hurricanes Matthew and Florence were similar, with only slightly more flow
and a higher gage elevation occurring during Hurricane Florence. Post-event aerial imagery
showed that flooding extents were also reasonably comparable between the two events, althoughmitigation efforts by the City of Lumberton post-Hurricane Matthew likely played a role inchanging the flood dynamics, including how long the flood waters stayed in the area during andafter Hurricane Florence. Door-to-door damage assessments recorded widespread damage anddisruption throughout the community for both events. However, flood depths measured over alarge proportion of the community, as well as reported morbidities and mortalities, were much
lower for Hurricane Florence.
The Wave 3a damage surveys revealed approximately two-thirds of the sampled housing units thatsustained any damage suffered more damage after Hurricane Matthew than Hurricane Florence.
In total, approximately 18 % of sampled housing units and 15 % of sampled businesses sustainedsome damage from Hurricane Florence. The reduction in flood extents and damage is likely due
to city-wide and individual-level mitigation actions taken ahead of Hurricane Florence, including
temporary berms placed strategically at the levee, surrounding the Lumberton Water Plant and anelectrical power network substation, and the deployment of hydraulic pumps at strategic points
around the City. Some households and businesses elevated interior contents and evacuated in
immediate preparation, and had also elevated exterior air conditioning condenser units andrelocated ductwork from crawlspaces to attics. The measurable reduction in the impacts fromflooding demonstrates the potential benefits of community-level preparation and individual-level
flood mitigation efforts as a result of the lessons learned from a recent flood.
While it is clear that less damage was sustained during Hurricane Florence than during Hurricane
Matthew in Lumberton, further investigation and analyses is needed to resolve sources of
uncertainty in damage measurements across events, including uncertainty due to sparse andtherefore neglected wind damage, faint or absent high-water marks, as well as evidence of multiple
high-water marks from both Hurricane Matthew and Hurricane Florence flood events. In limited
cases it remains unclear if damage attributable to Hurricane Florence was sustained or incurred inpart due to incomplete repair of damages from Hurricane Matthew.
The Wave 3a damage investigation provided an opportunity to utilize new measurement science
approaches. In particular, the field study team employed methods to help discern between damage
from each of the two hurricane events. These tools may become important for future field study
teams in the wake of climate change, causing compounding and concurrent impacts from multiple
events, for instance when a second extreme event occurs before a community fully recovers froma first extreme event. In the case of Wave 3a damage investigation, the team needed situational
flagging of compounded damage. Future research is needed to (1) develop the measurement
science to capture unrepaired damage compounded by a second extreme event; (2) enable better
determination of new versus old damage, including damage caused by deferred maintenance; (3)investigate how the duration of flood inundation leads to differential damage; (4) identify and86communicate easily-adoptable mitigation strategies that have major payoffs, such as elevating air
handling units; and (5) shed light on inherent modeling consequences stemming from the adopted
measurement approach.

5.2 Conclusions for Housing Disruption and Recovery
The Wave 3c housing survey provided a detailed picture of household experiences immediately
after Hurricane Florence, the actions taken in preparation for Hurricane Florence, and the status ofrecovery since Hurricanes Matthew and Florence. Counting abandoned units as survey
completions provides a 71 % response rate; not counting abandoned units and only considering
new and continuous residents who responded to the survey questions, the response rate was 34 %,which is considered satisfactory when compared to other social science field survey datacollections. What is concerning about Lumberton is the proportion of the sampled units (25 %)that were abandoned in Wave 3c, signaling very slow recovery from Hurricane Matthew further
exacerbated by Hurricane Florence.
Immediately prior to Hurricane Florence, approximately one-third of households reported
unrepaired damage from Hurricane Matthew. After Hurricane Florence nearly 85 % of the housing
sample reported lost power, smaller but still significant portions reported lost water (45 %),internet (45 %), sewer (33 %), landline phone service (34 %), and cell phone access (13 %).
Approximately two-thirds of the sample were dislocated from their home for at least one night
after Hurricane Florence, with dislocation times being longer, on average for residents of singlefamily dwellings and for homeowners, as opposed to multi-family dwellings and renters. Of thehouseholds that were dislocated, and the team was able to survey, a majority returned within two
weeks. Overall, shelters were not open as long after Hurricane Florence as after Hurricane
Matthew. For the 132 households who had completed repairs at the time of the survey, it took theman average of two months to complete repairs after Hurricane Florence while 38 % of respondents
still had unrepaired damages. Work, school, access, and household stability were also all disrupted
in Lumberton after Hurricane Florence.
Small proportions of respondents received insurance payouts and other recovery resources, with
FEMA IHP funds received by the largest proportion of households compared to all other resources
inquired about. The distribution of recovery funding was, on average, faster after Hurricane
Florence than Hurricane Matthew. Nevertheless, two-thirds of respondents reported unmet needs
which can be due to the slow arrival of relief funds from Hurricane Matthew. Although recovery
was still underway at the time of the survey, 85 % of respondents reported intentions of remaining
in their home for the next year, and more than 80 % indicated having the same access to school
and grocery stores after Hurricane Florence compared to before Hurricane Matthew.
The Wave 3c housing survey asked about the impacts of the two events on community cohesion
as well. Respondents were asked about changes in their involvement with their community
compared to before Hurricane Matthew. Approximately one-third of homeowners and renters
indicated increased community involvement and approximately 40 % indicated increased contact
with neighbors and extended family. Both of these factors are positive indications of social capital,
and thereby community resilience. Many households made a disaster plan with their household
members before Hurricane Florence. After Hurricane Florence, 40 % more households elevatedtheir hot water heater and/or HVAC, 20 % more households elevated interior contents like87

furniture and valuables to protect them from flood waters, 32 % had their home assessed by astructural engineer, and 60 % re-routed ductwork from below the home’s floor and into the attic
space, as compared to before Hurricane Florence.
Longitudinal comparisons about housing and household recovery are provided throughout Chapter
3. Re-occupancy and consistent abandonment are also reported for the entire 861 housing units inthe weighted sample. In Waves 1, 2, 3a, and 3c, respectively 12 %, 18 %, 23 %, and 25 % of thesample housing units were recorded as abandoned, whereas 88 %, 81 %, 52 %, and 74 % werereported as occupied, respectively. These findings indicate a gradual increased number ofabandoned homes in Lumberton since Hurricane Matthew as a result of failure to restore thoseunits.

5.3 Conclusions for Business Interruption and Recovery
The Wave 3c business survey collected data on the impacts of Hurricane Matthew and Hurricane
Florence, mitigation and other planning activities undertaken across the survey period, andresulting interruption and longitudinal recovery from both Hurricanes Matthew and Florence.
Including all businesses that were part of the Wave 3c sample, which includes closed businesses,
the survey response rate was 49 %. This response rate is relatively high when compared to othersocial science field survey collections and similar to the response rate for the Wave 2 Businesssurvey (52 %).
Before Hurricane Florence, about 5 % of businesses surveyed reported expecting to never fully
recover or to be in continued survival or response mode due to Hurricane Matthew. An additional
68 % of respondents reported being fully recovered from Hurricane Matthew immediately before
Hurricane Florence. Finally, before Hurricane Florence, repairs were still outstanding for 17 % of
the sample.
Seventy-three percent of businesses observed in Wave 3a were open, 19 % were closed, and 17 %had moved to a new location, either within or outside Lumberton city limits since Hurricane
Matthew. Due to oversampling in the inundation zone to better understand the differences in thenumber of businesses still recovering from Hurricane Matthew and affected by Florence, future
models can statistically control for covariates. The majority of business reported temporary utility
loss: electricity (80 %), water (27 %), internet (45 %), sewer (14 %), landline phone service (39%), and cell phone access (14 %). However, in all cases, a majority of sampled businesses reported
no or minor damage from Hurricane Florence. Some businesses experienced damage to theirbusiness location; these categories and the associated sample percentages are as follows: flood
damage to the building (21 %), wind damage to the building (30 %), contents damage (31 %), andmachinery damage (16 %).
The Wave 3c business survey also sought to measure business recovery using a variety ofindicators, following from the Wave 2 recovery measures. Most businesses (78 %) reported nochange in revenue as a result of Hurricane Florence. Decreased revenue was the second-highest
reported category (10 % of the responding sample). Most businesses reported operational
interruptions for some period during the first week post-Hurricane Florence, with the number ofbusinesses interrupted declining in subsequent weeks.

88A relatively small of businesses adopted mitigation, preparedness, or adaptation measures before
Hurricane Florence. For those who own their business building structure, these include
professional floodproofing of building (2 %), floodproofing of building by a non-professional (14%), secured a secondary storage location (14 %), and had the building structurally accessed (6 %).
For those who either own or rent their business premises these options include performing a risk
assessment (4 %), strategies to stay informed of weather warnings (13 %), development of an
emergency plan or checklist (4 %) and performing emergency drills (4 %). Some businesses
indicated taking one or more of these steps.
A major challenge for the Wave 3c survey effort was differentiating between the experience of thetwo Hurricanes, especially disentangling impacts. This was a challenge for both the researchers in
the field and for survey respondents.

5.4 Next Steps for the Lumberton Longitudinal Field Study
Wave 3 data collection occurred in three phases with associated field deployments. Wave 3a was
focused on initial reconnaissance post Hurricane Florence, Wave 3b focused on a qualitative studyof public housing, and Wave 3c focused on follow-up longitudinal survey efforts for households
and businesses. The data collection in Wave 3 presented unique challenges to the original
objectives of the longitudinal field study. Recovery following Hurricane Matthew was disrupted
for the Lumberton community by a second extreme flood event associated with Hurricane Florence
just two years later. Systematically and decisively tracking recovery from Hurricane Matthew andunderstanding and controlling for the damage and disruption from Hurricane Florence became thefocus of the Wave 3 data collection. A second major objective of the longitudinal field study is toadvance the area of field study metrology with a focus on community resilience. The successive
events in Wave 3 expanded the team’s ability to advance metrology in a way that can become
widely applicable in the wake of climate change.
The Lumberton longitudinal field study is planned to continue. As the dataset is built over time, it
is anticipated that the field study will make major contributions to community resilience andrecovery modelling efforts that are generalizable to other communities. In order to accomplishthese long-term objectives, set out by the research team, the following steps are recommended:
1. Both the housing and business survey collections continue in-person, as feasible due tohealth and safety, every 12-16 months with improvements and appropriate temporallybased changes made, as needed. The surveys should not, however, be lengthened, in aneffort to maintain response rates and minimize burden and survey fatigue on respondents.
Key questions or sections should be replicated throughout surveys to ensure comparability
across timeframes.
2. Interviews with key community stakeholders should continue every 12-18 months toprovide context for the findings of the field observations and structured data collections
through surveys.
3. Temporal analyses should be conducted comparing results across time, advancing thecross-wave comparisons made in Chapters 2, 3, and 4 of this report.
4. Ongoing analysis should be conducted to ensure that the field study continues to align withwhat is needed for NIST measurement and modeling, and improvements to standardized
field study protocols. This includes the use of data in: (1) development of the Alternatives

89

for Resilient Communities (ARC) model, a web-based tool to assist in community
resilience planning, (2) a case study linking ARC and the NIST Economic Decision Guide
Software (EDGe$) Tool Online, (3) for recovery measurement to inform the Tracking
Community Resilience (TraCR) Methodology, and (4) accounting for disaster losses witha focus on major indirect losses, such as business interruption, and distributional effects.
5. A testbed of Lumberton should be developed and incorporated into IN-CORE for moredetailed numerical analysis that incorporates the state-of-the-art resilience science andmodeling from Center and NIST researchers with the novel data collection through theLumberton longitudinal field study.
6. Housing and business survey instruments, IRB protocols and supplemental documents, andthe collected data should continue to be published and receive Digital Object Identifier(DOI). The on-going collection can be found on DesignSafe-CI: https://www.designsafeci.org/data/browser/public/designsafe.storage.published/PRJ-2656

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Lumberton, North Carolina Flood: An Interdisciplinary Approach”. ASCE Natural
Hazards Review, 2020, 21(3): 04020029.
Xiao, Y., Watson, M., Helgeson, J., Farokhnia, K., van de Lindt, J., Mitrani-Reiser, J., Sutley, E.,
Deniz, D., Tomiczek, T., Barbosa, A., Fung, J., Nofal, O., Koliou, M. (2020). "Business
Survey Instrument, January 19, 2018: Wave 2," in A Longitudinal Community Resilience
Focused Technical Investigation of the Lumberton, North Carolina Flood of 2016.
DesignSafe-CI. https://doi.org/10.17603/ds2-f9kt-fm93

92

Appendix 2A – Wave 3a Survey
North Carolina Flood Field Study Damage Inspection Tool
2018 Draft developed by Elaina Sutley and Shane Crawford, October 5, 20182016 Draft developed by Derya Deniz, Tori Johnson, and John van de Lindt, November 16, 2016
Please fill in the fields and circle the option that best identifies the condition for multiple-choice questions.
SURVEYOR NAMES:(list all)

BUILDING ID:______________
Building Address:_____________________________ID Photo #:Building Type:(circle one)

Date/Time of survey:___________________________(1) ____________(2)____________
Single Family
Multi-Family Attached Business(3+ units)(e.g., strip mall)
Mobile Home

DuplexWood

Foundation Type:Slab on grade

Concrete

Masonry

GPS-E:__________(4)____________(5)_______

If Residential, is there a garage? Y or N:______
If Y, how many cars fit in the garage? 1, 2:____

Standalone BusinessEnter Number of above ground stories (e.g., 1, 2, 3):___________
Construction Type:(3)___________
Other building type, Explain:

GPS- N:_________Is there a basement? Y or N:__________
Steel

Crawlspace

Does the building appear to be well-maintained? Y, N or N/A:__________

Pre-fab metal

Other, Explain:

Split

Other, Explain:

Comments:

FLOOD INFORMATION
Flood level w.r.t to ground:High water mark location (HWM):

Height between Ground level and FFE:
FFE=threshold of front or rear door___________inches
Foundation

First Floor

Second Floor___________inches

Other, Explain:
A-1

OVERALL DAMAGE CLASSIFICATION: (circle one)(refer to either the residential or business damage state descriptions card)
Have interior contents orfinishes been removed?

DS0

DS1

DS2

DS3

DS4

YES Date*_________NO

Is there evidence of current occupancy? Y or N: ___________(based on inspection only)

Has repair work started?
YES Date*_________*Date can be collected for businesses only without IRB.

NO

Is mold present? Y or N: _____________

EXTERIORDAMAGE

Foundation: (circle one)

DS0

DS1

DS2

DS3

DS4

Walls: (circle one)

DS0

DS1

DS2

DS3

DS4

Attachments or Detached Structures: (circle one)

DS0

DS1

DS2

DS3

DS4

DS0

DS1

DS2

DS3

DS4

OVERALL INTERIOR DAMAGE CLASSIFICATION: (circle one)Mark the damage level for the interior items below, where ruined requires full replacement. If item is not applicable or assessable, mark accordingly.

Interior Items

Plywood Subfloor
Flooring
Carpet Pad
Base Trim
Water Heater
Furnace
HVAC equipment
Water Softener
DrywallElectrical Outlets
Base Cabinets (Bathroom)
Upper Cabinets (Bathroom)
Countertop (Bathroom)
Bathroom Facilities
Base Cabinets (Kitchen)
Upper Cabinets (Kitchen)

Not Applicable

Not Assessable

No DamageLightly Damaged

RuinedA-2

Countertop (Kitchen)
Electrical Panelboard
StaircaseContinue marking damage level for the interior items below, where ruined requires full replacement. If item is not applicable or assessable, mark accordingly.
Front door
Interior doors
Windows
Lighting fixtures
Ductwork
Kitchen appliances (e.g., dishwasher)
Furniture (including nail stations, medical beds)

Inventory

Not Applicable

Not Assessable

No DamageLightly Damaged

RuinedMachinery

Not Applicable

Not Assessable

No DamageLightly Damaged

RuinedPerishable inventory for sale (e.g., temperature- or
electricity-sensitive; e.g., food, drink, medication)
Non-perishable inventory for sale (not sensitive totemperature or electricity; e.g., construction materials)
Documents / records
Molds (manufacturing)
Racks, shelving, storage container
Office supplies (e.g., paper, pens)
Computer
Printers
Cash register
Manufacturing equipment
PhonesRefrigerator
Escalator / elevator
Heavy machinery (construction)

EXTRA NOTES:Explain condition of the drinking water well if there is any around the house: _________

A-3

Appendix 2B - Overall damage descriptions
Lumberton Field Study
Residential Damage Survey

Descriptions of Residential Damage States
DS Level

Description0

No damage; water enters crawlspace or touches foundation (crawlspace or slab on grade). No contact toelectrical or plumbing, etc. in crawlspace. No contact with floor joists. No sewer backup into living area.

1

Water touches floor joists or minor evidence that water enters house; damage to carpets, pads,
baseboards, flooring. Approximately 1” in house but no drywall damage. Could have some mold (<10%mold coverage) on subfloor above crawlspace. Could have minor sewer backup and/or minor moldissues.

2

Water level approximately 2 feet with associated drywall damage and electrical damage, water heater andfurnace and other major equipment on first floor damaged. Lower bathroom and kitchen cabinets
damaged. Doors or windows may need replacement. Could have major sewer backup and /or major(>10% mold coverage) mold issues.

3

Water level 2 feet to 8 feet; substantial drywall damage, electrical panel destroyed, bathroom/kitchencabinets and appliances damaged; lighting fixtures on walls destroyed; ceiling lighting may be ok. Studs
reusable; some may be damaged. Could have major sewer backup and/or major mold issues.

4

Significant structural damage present; all drywall, appliances, cabinets etc. destroyed. Could be floatedoff foundation. Building must be demolished or potentially replaced.
Table 1. Overall damage description for residential buildings

A-4

Exterior Damage

DS LevelFoundation:

Walls:

Attachments or
Detached
Structures:
Garage/Porches/Sheds…

0
Water enterscrawlspace or
touches
foundation but novisible damage.
Water may or may
not touch walls
but no visible
damage.
Water touches
exterior of garage
or porch but novisible damage.

1
Waters enterscrawlspaces but not
any significant
damage. Just watermarks/ mud.
Water touches walls
but no damage on
the wall or cladding
or insulation, justaestheticmarks/mud.
Visible damage orwater marks/mud.

2

3

Minor cracks onfoundation stem walls.

Cracks or holes onfoundation stem walls.

Need to clean and dry
the wall out. Slightdamage on insulation
or cladding which
need partial
replacement.

Water penetration
through holes or cracks
on the walls. Or water
penetration throughbroken windows. Studs
reusable when dried.

Minor damage togarage door/ minor
damage on decks.

Major damage on
garage door or on decks(i.e. garage door needs
replacement).

Drywall damage up to
Substantial drywall
2 feet and electrical
Water enters thedamage, electricaldamage, heater andfoundation but nopanel destroyed,
furnace and othercontact or nobathroom/kitchen
major equipment on
visible damage tocabinets and appliances
Overall Interior Damage
floor damaged; Lower
electrical or
damaged; lighting
bathroom and kitchen
plumbing, or floor
fixtures on wallcabinets damaged.
joists.
destroyed; ceiling
Doors needlighting may be ok.
replacement.
Table 2: Detailed damage descriptions for external and internal (residential) building components
Water enters house;damage to carpets,
pads, baseboards,
flooring, but nodrywall damage.
Touches joists.
Could have somemold on subfloor
above crawlspace.

4
Major structural damage
on foundation.
Differential settlement or
the structure floated off
the foundation.
Significant structural
damage present orcollapse; majority ofwalls damaged beyond
the point of reuse.
Major or significant
structural damage
present; floated away or
destroyed.

All drywall, ceilinglights, appliances,cabinets etc. destroyed
and need replacement.

A-5

Lumberton Field Study
Business Damage Survey

Descriptions of Business Damage States
DS Level

Description0

No damage; No contact to electrical or plumbing, etc. in crawlspace. No contact with floor joists. No
sewer backup
Water touches floor joists or minor evidence that water enters building; damage to carpets, pads,
baseboards, flooring. Approximately 1” in the building but no drywall damage. Could have some moldin crawlspace. Could have minor sewer backup and/or minor mold issues (<10% mold coverage).
Water level approximately 2 feet with associated drywall damage and electrical damage, water heaterand other major equipment. Doors or windows may need replacement. Could have major sewer backup
and /or major mold issues (>10% mold coverage).
Water level 2 feet to 8 feet; substantial drywall damage, electrical panel destroyed, office cabinets or
storage racks; lighting fixtures on walls destroyed; ceiling lighting may be ok. Studs reusable; some may
be damaged. Could have major sewer backup and/or major mold issues.
Significant structural damage present; all drywall, cabinets etc. destroyed. Could be floated off
foundation. Building must be demolished or potentially replaced.
Table 1: Overall damage descriptions for business buildings

1
2
3
4

A-6

Exterior Damage

DS LevelFoundation:0
Water enterscrawlspace or
touches
foundation but novisible damage.

Walls:

Water may or may
not touch walls
but no visible
damage.

1
Waters enterscrawlspaces but
aesthetic damage
only (e.g., watermarks/ mud.
Water touches walls
but no damage on
the wall or cladding
or insulation, justaestheticmarks/mud.

Attachments or
Detached
Structures:
Garage/ Loading
Dock/Sheds…

Water touches
exterior ofstructure but novisible damage.

Visible damage orwater marks/mud.

Overall Interior Damage

Water enters thefoundation but nocontact or novisible damage toelectrical orplumbing, or floor
joists.

Water entersbuilding; damage tofinishes, but no walldamage. Touches
joists. Could havesome mold on
subfloor above
crawlspace.

2

3

Minor cracks onfoundation stem walls.

Cracks or holes onfoundation stem walls.

Need to clean and dry
the wall out. Slightdamage on insulation
or cladding requiring
partial replacement.

Water penetration
through holes or cracks
on the walls or throughbroken windows. Studs
reusable when dried.

Minor (repairable)
damage to structure
door/ minor damage
on deck or loading
docks.
Drywall damage up to
2 feet and electricaldamage, heater andfurnace and othermajor equipment on
floor damaged; Lowercabinets or shelving
damaged. Doors needreplacement.

Major (irrepairable)
damage on structure
door or on loading dock(i.e. needs
replacement).
Substantial drywall
damage, electrical
panel destroyed,bathroom facilities,
shelving, andequipment damaged;
lighting fixtures on wall
destroyed; ceilinglighting may be ok.

4Major structural damage
on foundation.
Differential settlement or
the structure floated off
the foundation.
Significant structural
damage present orcollapse; majority ofwalls damaged beyond
the point of reuse.
Major or significant
structural damage
present; floated away or
destroyed.

All drywall, ceilinglights, equipment andmachinery, cabinets andshelving, etc. destroyed
and need replacement.

Table 2: Detailed damage descriptions for external and internal (business) building components

A-7

Appendix 2C – Wave 3a Information sheet
Hurricane Community Recovery Study for Lumberton, NC
Center for Risk-Based Community Resilience Planning
A U.S. National Institute of Standards and Technology-funded Center of Excellence_______________________________________________________________________The Center for Risk-Based Community Resilience Planning is based at Colorado State University inFort Collins, Colorado, and includes collaborations with researchers from universities across theUnited States. The National Institute of Standards and Technology’s Community Resilience
Program is based in Gaithersburg, Maryland and includes engineers, economists, and sociologists.
Collectively, we are working to understand what makes a community “resilient” – or able tobounce back – in the face of disaster.
Our research in Lumberton focuses on community recovery following the flooding that occurred
due to Hurricane Matthew in early October 2016, and after Hurricane Florence in September
2018. We are returning to Lumberton October 16-19, 2018 to follow up our first two studies
performed in November 2016 and January 2018. We will be collecting information on the physicaldamage to residential and commercial buildings to gain knowledge on the initial physical impact
from Hurricane Florence and the resulting flooding. We will return in January 2019 to speak withhouseholds and businesses to learn more about the disruption experienced following two
successive hurricane and flooding events. We hope to learn from your experiences to help
communities better prepare for similar events in the future.
During the October trip, we will be collecting measurements of flood height and information on
visible physical damage to residential and commercial structures, including to the building
exterior, foundation, walls, and interior items if visible.
This research is part of a five-year project that will be carried out by experts from engineering, thesocial sciences, economics, and many other disciplines. This community was selected as one of
our five research locations around the country that we hope to learn from. Our field team will bein Lumberton from October 16 through 19, 2018, and expects to return in January 2019.
If you have more general questions about the project or the Center for Risk-Based Community
Resilience Planning, please contact: Dr. John van de Lindt at 970-218-4076 or via email:jwv@engr.colostate.edu.

A-8

Appendix 2D - Handouts available to sample
Mental Health Resources
Should you need additional assistance, the following local resources are available to provide
timely services.
Crisis Services for Robeson County are managed by: Eastpointe
Phone Access:Eastpointe Access Center is available 24 hours a day, 7 days a week. Customer Service
Specialists will assist you to find a crisis provider that is well-matched with your needs. Your
local number is: 800-913-6109 or for TTY 888-819-5112
If you already have a service provider, call them first. Providers who know you are usually best
prepared to assist you in a crisis.
Hurricane Florence Resources:
http://www.eastpointe.net/home/hurricane-florence-related-information/
State Level Resources:
If you’re feeling anxious, stressed or need to talk, call the state’s behavioral health “Disaster
Distress” hotline at 1-800-985-5990.
You can also request to talk to a crisis counselor with Magellan Health. Call the crisis
information and resource hotline at 1-800-327-7451. Magellan can also give referrals to local
shelters, food and other services.
Trillium Health Resources supports mental health in 26 eastern North Carolina counties. Callthem at 1-877-685-2415 to connect with mental health resources.
Parents and caregivers can access resources on how to help children recover after the hurricaneon the website of the National Child Traumatic Stress Network: https://www.nctsn.org/what-ischild-trauma/trauma-types/disasters/hurricane-resources.

A-9

Appendix 3A – Wave 3c Housing Survey
OMB Control #0693-0078
Expiration 07/31/2019

Surveyor(s):_________________________________________ Date:________________________________________
Building ID:______________ Wave 2 DS: _____ Repaired in Wave 2?_____ Unit Address:________________________
Building Type: 1, Single family 2, Multi-family, # housing units _______

3, Mobile home

4, Other, _____________

Comments:_________________________________________________________________________________________
Result/Completion
Codes

1. Completed interview
2. Ineligible, no adult or eligible person to answer questions
3. Bad address, could not locate HU
4. Incomplete/partial
5. Not occupied residence, abandoned; home destroyed
6. Ineligible, structure not a residence
7. No answer or response, but evidence or confirmed occupied
8. No access, gated community, fence preventing entry

Housing Unit
Occupancy status:

YES: household presentinterviewed or attempted

YES, household not present;
evidence of habitation

DK: Indeterminate/
uncertain

NO: not occupied, appears
abandoned

YES, household not present;
occupied confirmed by neighbor

YES, household not present but
occupied, confirmed bymanagement

NO: not occupied, underrepair/ reconstruction.

NO, damaged and not habitable

If interview not possible but neighbors, apartment managers, or others can provide information, record here:
Mark type of informant: Was the housing unit occupied at theIs the household still living there?
time of HURRICANE FLORENCE?
Neighbor
YESNO
DK
YES, #HH members______ NO
DKManager
Other: (specify)
If not still living there, do you know when the household left?
____________MM/YY________________
Did household leave because of HURRICANE FLORENCE flooding? YESNO DK[The following questions are to be answered upon consent of a household (HH) member over 18 years of age.]
1. How many people live in your household?
2. When did you move into this home?
IF AFTER SEPT 2018, SKIP TO Q26.
3. Was this house damaged from HURRICANE MATTHEW?
3a. If YES, how much of your HURRICANE MATTHEW
damage was fully repaired at the time of HURRICANEFLORENCE?
4. Was this house damaged from HURRICANE FLORENCE?
4a. If YES, how long until it was fully repaired?

Adults (≥ 18) ______

Children (< 18) ______________________ MM/YY
YES
NO
None

Some
YES

_________ D/WK/MO

DK
Most

NO

All

DK
Still not repaired

The next set of questions are intended to capture the impacts of HURRICANE FLORENCE and the flooding on your HH.
5. 5. Did your home lose electric
power?

YES

NO

DK*

If yes, for how long?

Record in hours or days*______ Hrs. ______ Dys

6. a. Did your home lose water?

YES

NO

DK*

If yes, for how long?

Record in hours or days*______ Hrs. ______ Dys

6b. Did your home lose
wastewater/sewer?

YES

NO

DK*

If yes, for how long?

Record in hours or days*______ Hrs. ______ Dys

7. Was your water unsafe to drink
or under a boil water order?

YES

NO

DK*

If yes, for how long?

Record hours or days*______ Hrs. ______ Dys
A-10

Did you home lose gas (LP)?

YES

NO

NA

DK*

If yes, for how long?

Record hours or days*______ Hrs.
______ Dys

9. a. Did your Household lose cell
phone service?

YES

NO

NA

DK*

If yes, for how long
without cell phoneservice?

Record hours or days*______ Hrs. ______ Dys
Record hours or days*______ Hrs. ______ Dys
Record hours or days*______ Hrs. ______ Dys

8.

9. b. Did your Household lose landline phone service?

YES

NO

NA

DK*

If yes, for how long
without landline
phone service?

10. Did you lose any internet
connectivity to your home?

YES

NO

NA

DK*

If yes, for how long
without internet
service?

NA = not appropriate question, did not have this utility
DK* = don't know because were out of their home when utility may have been off.
If the household evacuated, and never lost cell service, put NO*Record hours or days, but not both.
Record hour, if utility disruption for less than a day.
Record days, to nearest day, if disruption over a day.

11. Were you and your household dislocated from your home?
[If YES, continue to a-e. If NO, skip to Q12.]
a. How long were you and your household dislocated from your homebecause of the flood following HURRICANE FLORENCE? (insert time range, and

YES

NO

DK

______________ D/WK/MO

circle whether in D, WK, or MO)

b. Did the timing of insurance payout or other financial assistance cause
a delay in your household returning home?

YES

NO

DK

c. Did a closure or change in your place of work cause a delay in yourhousehold returning home?

YES

NO

DK

d. Did a closure or change in your child/ren’s school cause a delay inyour household returning home?

YES

NO

DK

e. Did closure of businesses, such as day care or grocery stores, cause adelay in your household returning home?

YES

NO

DK

12. Since the beginning of 2018, did you or any household members misswork due to housing issues caused by the flooding? [housing issues are
any problems with your house that started with HURRICANE
MATTHEW/HURRICANE FLORENCE and/or the flooding]

YES

NO

DK

a. If YES, what was the greatest number of days/weeks of work missedby household members:__________________D/WK

b. If YES, where does the person who missed the greatest number ofdays/weeks work?

_______________________The next set of questions are about assistance you may have received to help with your recovery following
HURRICANE FLORENCE.
13. Does your household OWN or RENT this house?
a. If RENT, did you have renter’s insurance prior to
HURRICANE FLORENCE?
b. If OWN, did you have flood insurance prior to
HURRICANE FLORENCE?
c. If OWN, did you have homeowner’s insurance prior to
HURRICANE FLORENCE?
d. If OWN, did you have a mortgage prior to HURRICANEFLORENCE?

Own

Rent

Other, specify_________

YES

NO

DK

YES

NO

DK

YES

NO

DK

YES

NO

DK
A-11

14. If YES to insurance (Q13a-c), did you receive a payout for damages from HURRICANE FLORENCE flooding?
[Circle all that apply] YES, from renters ins. YES, from flood ins. YES, from homeowner’s ins.
NO
DK
a. If YES to receive payout (Q14), when did you receive your insurance payout(s)?
(Date MM/YY)_____________________
b. If YES to receive payout (Q14), how many of your repairs were covered by insurance payout(s)?
Very little Some Almost all/All
15. If NO to insurance (Q13a-c) before HURRICANE FLORENCE, do you have insurance now?
[Circle all that apply] YES, renters ins.
YES, flood ins.
YES, homeowner’s ins.

NO

DK

[Homeowners continue to next question; Renters skip to Q18.]
Now I am going to ask you about seven types of assistance to learn what resources were available to community
members. Please answer whether you applied or received any of these types of assistance since the beginning of
2018, considering either HURRICANE MATTHEW or HURRICANE FLORENCE.
16. For each of the following types of disasterrecovery assistance, did you apply for, and/or
receive the following:a. Home repair funds from FEMA (IHP)
b. SBA (Small Business Administration) loansc. Home repair grant (HUD/CDBG-DR) fromgovernmentd. Hazard Mitigation Grant Program from
FEMA (HMGP) (e.g., elevations, buyouts,
demolition, rebuild)e. Financial assistance from non-governmentalorganizations?
f. Clean up or repair help from nongovernmental organizations?
g. Financial assistance from friends or familyh. Cleanup or repair assistance from friends orfamilyWave 2
responses

Applied? Received? HM* HF*(Y or N)(Y or N)(X)
(X)

If received,
when?
MM/YY

*mark (X) if they applied and/or received from either/both hurricane (HM=Matthew; HF=Florence)

17. If you received any of the above types of assistance and/or insurance
payouts, was the total amount you received enough to pay for repair
and replacement of all of the physical damages to your home andcontents considering both HURRICANE MATTHEW & HURRICANEFLORENCE?
a. If NO, how many of your repairs/replacement were covered?
b.

If NO, did you and your household personally pay for the rest?

YES

NO

DK

Very little

Some

Almost all/all

YES

NO

DK

Now I am going to ask you about your assessment of recovery from HURRICANE MATTHEW and HURRICANEFLORENCE..
18. Does your household have the same access to school in this home as
YES
NO
DK
you did before HURRICANE MATTHEW and HURRICANE FLORENCE?
A-12

19. Does your household have the same access to work in this home as
you did before HURRICANE MATTHEW and HURRICANE FLORENCE?

YES

NO

DK

20. Does your household have the same access to grocery stores andother essential needs in this home as you did before HURRICANE
MATTHEW and HURRICANE FLORENCE?

YES

NO

DK

21. Has your involvement with neighborhood and/or community groups
increased since HURRICANE MATTHEW?

YES

NO

DK

YES

NO

DK

YES

NO

DK

22. Has your contact with neighbors and/or extended family and friends
increased since HURRICANE MATTHEW?
23. Does your household plan to move to a different housing unit within
the next year because of any issue that unfolded as a result of
HURRICANE MATTHEW and/or HURRICANE FLORENCE?
a. If YES (to Q23), do you have any specific area in mind to move?

Somewhere Somewhere
else inelse in NC
Lumberton

Out ofState[See Q1, if 0 children in household, skip to Q26] Next, I want to ask about the experience of the children in your HH.
24. Was your child/ren enrolled in a school in Lumberton in the past 12 months?
Yes No [If NO, skip to Q26]
25. When thinking about your child/ren’s educational recovery following both hurricanes, would you say that yourchild/ren’s educational situation is…
a. Better than it was before the hurricanes
b. Back to where it was before the hurricanes
c. Worse than before the hurricanes
d. Uncertain, things are still changing for you child/rene. Other (Please specify)___________________________________________________________
26. The next set of questions looks at mitigation andpreparedness strategies used in this house before or since
Hurricane Florence. Did your household:a. Elevate hot water heater and/or HVAC
b. Elevate interior contents in preparation for the floodc. Assess the building (structurally) by an engineer
d. Re-route ductwork from below floor to attic spacee. Make disaster plan with household members
f. Other:g. Other:h. Other:

Had before Florence
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

No
No
No
No
No
No
No
No

Have now

DK
DK
DK
DK
DK
DK
DK
DK

Yes No DK
Yes No DK
Yes No DK
Yes No NA*
Yes No DK
Yes No DK
Yes No DK
Yes No DK

Finally, I have four questions about your household in general.
27. When considering all members in your household,what is the highest number of years of schoolingcompleted?
28. While we often ask about each member of ahousehold, in general, when considering yourhousehold how would you characterize its racial
makeup? Select one or more.
29. Are members of your household Hispanic or
Latino?
30. Finally, I don’t want to know the exact amount,but can you identify the letter associated with thecategory that best captures your household’s
combined annual income? (hand respondent theincome card)

High School

Associate’s degree

1) White
2) Black or African American3) American Indian or Native American4) Asian

Bachelor’s degree

Master’s deg. or
higher5) Native Hawaiian or other Pacific Islander
6) [If respondent selects >1, surveyor mustspecify codes] _______________

1) Not Hispanic or Latino 2) Yes, Hispanic or Latino
A. $1 to $3,999
B. $4,000 to $5,999
C. $6,000 to $7,999
D. $8,000 to $9,999
E. $10,000 to $11,999

F. $12,000 to $14,999
G. $15,000 to $19,999
H. $20,000 to $24,999
I. $25,000 to $29,999
J. $30,000 to $39,999

K. $40,000 to $49,999
L. $50,000 to $74,999
M. $75,000 to $99,999
N. $100,000 to $149,999
O. $150,000+
A-13

Thank you for your time!

This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the Office ofManagement and Budget (OMB). Notwithstanding any other provisions of the law, no person is required to respond to,nor shall any person be subject to a penalty for failure to comply with, a collection of information subject to therequirements of the PRA unless that collection of information displays a currently valid OMB control number. For thiscollection, the OMB Control number is:0693-0078 with an expiration date: July 31, 2019. Public reporting burden for thiscollection is estimated to be 10 minutes per survey, including the time for reviewing instructions, searching existing data
sources, gathering and maintaining the data needed and completing and reviewing the collection of information. Send
comments regarding this burden estimate or any aspect of this collection of information, including suggestions for
reducing this burden, to the National Institute of Standards and Technology, Attn: Dr. Jennifer Helgeson, NIST, 100
Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301-975-6133, or via
email:jennifer.helgeson@nist.gov.

Privacy Act Statement
Authority: The Paperwork Reduction Act of 1980 (Pub. L. No. 96-511, 94 Stat. 2812, codified at 44 U.S.C. §§ 3501–3521).
Purpose: The National Institute for Standards and Technology (NIST) conducts Community Resilience research andsurveys through the Generic Paperwork Reduction Act Clearance, OMB CONTROL NO. 0693-0078 Expiration date:
07/31/2019.
Routine Uses: NIST will use this information to conduct a systematic process evaluation of the NIST Community
Resilience Planning Guide implementation. This is not a Privacy Act system of Records, therefore there is no System ofRecords Notice associated with this collection.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary
consent for NIST to use of the information you submit for the purpose stated.

A-14

Appendix 3B - Wave 3c Housing Information sheet
Hurricane Matthew Community Recovery Study for Lumberton, NC
Center for Risk-Based Community Resilience Planning
A U.S. National Institute of Standards and Technology-funded Center of Excellence_______________________________________________________________________The Center for Risk-Based Community Resilience Planning is based at Colorado State University
in Fort Collins, Colorado, and includes collaborations with researchers from universities acrossthe United States. The National Institute of Standards and Technology’s Community Resilience
Program is based in Gaithersburg, Maryland and includes engineers, economists, andsociologists. Collectively, we are working to understand what makes a community “resilient” –or able to bounce back – in the face of disaster.
Our research in Lumberton focuses on community recovery following the flooding that occurred
due to Hurricanes Matthew and Florence. We are returning to Lumberton for our third year tofollow up on progress with recovery. We will be collecting information from households likeyours to gain knowledge on the impacts and the recovery from Hurricanes Matthew andFlorence. We hope to learn from your experiences to help communities better prepare forsimilar events in the future.
This research is part of a five-year project that will be carried out by experts from engineering,
the social sciences, economics, and many other disciplines. This community was selected as oneof our five research locations around the country that we hope to learn from. Our field team will
be in Lumberton from April 11 through 21, 2019.
Because NIST is part of the federal government, this research was reviewed through a special
process. This collection of information contains Paperwork Reduction Act (PRA) requirements
approved by the Office of Management and Budget (OMB). Notwithstanding any otherprovisions of the law, no person is required to respond to, nor shall any person be subject to apenalty for failure to comply with, a collection of information subject to the requirements of the
PRA unless that collection of information displays a currently valid OMB control number. For thiscollection, the OMB Control number is: 0693-0078 with an expiration date of July 31, 2019.
Public reporting burden for this collection is estimated to be 15 minutes per survey, including
the time for reviewing instructions, searching existing data sources, gathering and maintaining
the data needed and completing and reviewing the collection of information. Send comments
regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology,
Attn: Dr. Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710,
telephone 301-975-6133, or via email:jennifer.helgeson@nist.gov
If you have more general questions about the project or the Center for Risk-Based Community
Resilience Planning, please contact: Dr. John van de Lindt at 970-218-4076 or via email: t
jwv@engr.colostate.edu.

A-15

Appendix 3C - Wave 3c Housing Consent Script
Two-Year Post-Hurricane Matthew Field Study in Lumberton, North Carolina
Housing/Household Recovery Survey
Consent Script
OMB CONTROL NO. 0693-0078 Expiration date: 07/31/2019

Hello, my name is (interviewer name) and I am a researcher from (name of university or National
Institute of Standards and Technology) in the (department name/Engineering Laboratory). We areconducting a research study on recovery following the flooding that occurred in Lumberton, N.C. from
Hurricane Matthew and Hurricane Florence. We would like to speak with you about how this event
affected your household. In particular, we are interested in learning about the process of recoveringfrom the flooding.
This study is part of a larger project led by Center of Excellence for Risk-Based Community Resilience
Planning at Colorado State University. This project is led by Drs. John van de Lindt and Bruce
Ellingwood, both Professors from the Civil and Environmental Engineering Department and is funded by
the National Institute of Standards and Technology (NIST).
We would like to ask you some brief survey questions about your household’s experience after the flood
as well as some details about your household during this time. Participation will take approximately
fifteen minutes. Your participation is voluntary. If you decide to participate in the study, you may
withdraw your consent and stop participation at any time without penalty.
We will be collecting information about the damage to your home, the process of making repairs, andhow the flood disrupted your household’s living arrangements and daily routines, such as going to workand school. When we report and share our findings, we will combine the data from all participants intosummary statistics and tables so no unique individual or household can be identified. There are NO
KNOWN RISKS or direct benefits to you. We hope to gain more knowledge on how you and others wereaffected by Hurricanes Matthew and Florence so that we can learn from your experiences to help
communities better prepare for similar events in the future.
So again, we would like to speak with an adult member of the household. Would that person be you
and would you be willing to participate?
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person isrequired to respond to, nor shall any person be subject to a penalty for failure to comply with, acollection of information subject to the requirements of the PRA unless that collection of information
displays a currently valid OMB control number. For this collection, the OMB Control number is:0693-0078
with an expiration date: July 31, 2019. Public reporting burden for this collection is estimated to be 10
minutes per survey, including the time for reviewing instructions, searching existing data sources,
gathering and maintaining the data needed and completing and reviewing the collection of information.
Send comments regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology, Attn: Dr.
Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301-9756133, or via email:jennifer.helgeson@nist.gov.

A-16

Appendix 4A – Wave 3c Business Survey
TWO-YEAR POST-HURRICANE FLORENCE FIELD STUDY IN LUMBERTON, NORTH CAROLINA
BUSINESS RECOVERY SURVEYOMB CONTROL #0693-0078; EXPIRATION DATE: 07/31/2019(NIST GENERIC CLEARANCE FOR COMMUNITY RESILIENCE DATA COLLECTIONS)
Date: ______________________

Surveyor(s): ________________________

PIN: _________________

Business Name: ____________________

Address: ___________________________________________________________
1. What is the operational status of this business?
1. Open
2. Permanently closed
3. Moved to alternative location (provide address:______________________________)
4. Not sure/don’t know:(take notes on any information that can help us identify the status of the business:_____________________________________________________________ )[THE FOLLOWING QUESTIONS SHOULD BE ANSWERED BY BUSINESS OWNER OR MANAGER]
2. What is your role with this business?

1. Owner 2. Manager 3. Owner and Manager

3. Does this business have any of the following federal classification? [Please circle all that apply]
1. Woman-owned

2. Minority certified by the SBA (8a)3. Veteran-owned

4. NONE“The first set of questions are intended to capture how recovery is going following the flooding in 2016 caused
by Hurricane Matthew. And to give us an idea of where your business was in recovery before Hurricane
Florence.”

FOLLOW-UP: HURRICANE MATTHEW
4. What kind of damage was caused by Hurricane Matthew and how severe was the damage? [hand damage
description card to business owner/manager]
Flood depth__________ inches

Building damage (due to flood)

1. None 2. Minor 3. Moderate 4. Severe 5. Complete

Building damage (due to wind)

1. None 2. Minor 3. Moderate 4. Severe 5. Complete

Contents/inventory damage

1. None 2. Minor 3. Moderate 4. Severe 5. Complete

Machinery/equipment damage

1. None 2. Minor 3. Moderate 4. Severe 5. Complete

5. Where do you feel your business was in the process of recovery immediately BEFORE Hurricane Florence?
1. Still in survival/response mode
2. Recovering
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3. Mostly recovered
4. Fully recovered
5. Still in operation but would never recover (please explain):_______________________________
6. What is the % capacity at which you were operating immediately BEFORE Hurricane Florence? ______ %[For “capacity,” ask the business to consider aspects of the business that are most important to them, like thequality and/or quantity of service or product offerings. For example: 50% for reduced capacity, 110% forincreased capacity, or 0% for businesses that have not resumed operations.]
7. How profitable was your business…
a. Before Hurricane
Matthew
b. Between Hurricane
Matthew and Florence

1. Highly
Profitable 2. Profitable
1. Highly
Profitable 2. Profitable

3. Breaking
Even
3. Breaking
Even

5. Highly
6. Closed
Unprofitable
5. Highly
6. Closed
4. Unprofitable
Unprofitable

3. Breaking
Even

4. Unprofitable

4. Unprofitable

How profitable is your business now?
c. Current profitability

1. Highly
Profitable 2. Profitable

5. Highly
Unprofitable

6. Closed

8. Do you still require physical repairs from Hurricane Matthew?
1. Yes
2. No 3. N/Aa. If YES, what percent of your repairs still need to be completed? ___________
9. How much would you say Hurricane Florence affected your recovery from Hurricane Matthew?
1. Not at all
2. A little
3. Moderately
4. Severely
5. Not applicable (not affected by Matthew)
10. How important are the decisions of other businesses (those on the same block as your business) toremain open or to close in your own decision-making?
1. Not at all important
2. Somewhat unimportant
3. Neither important nor unimportant
4. Somewhat important
5. Very important“Okay, thank you. The next question is going to ask about financial recovery resources to understand what wasor may not have been available to businesses in Lumberton.”

A-18

11. How did you finance your business's recovery from Hurricane Matthew? [Ask the business to indicate witha percentage for example, their recovery was funded 20% by credit card, 60% by personal savings, and 20% by
insurance.]
Personal savings
Credit card
Corporate assistance (or assistance from another branch/location)
Insurance
Donations
Private bank loans
Crowd funding
Assistance from friends or family
Federal assistance programs (e.g. Small Business Administration loans)(List:___________________________________________________________)
State assistance programs (e.g. Resilient Recovery Loan Program)(List:___________________________________________________________)
Local assistance programs (e.g. grant or loan from the city or local non-profit)(List:___________________________________________________________)
Other:
Other:
Other:TOTAL

100[Alternative, circle if the business doen’t know or cannot answer]:

DK

IMPACT AND RECOVERY FROM HURRICANE FLORENCE“These next series of questions are intended to capture information about the impact and recovery from
Hurricane Florence. Please think about the days or weeks immediately following Hurricane Florence whenproviding your responses.”
12. What kind of damage was caused by Hurricane Florence and how severe was the damage? [hand damage
description card to owner/manager]
Building damage (due to flood)
Flood depth

1. None 2. Minor 3. Moderate 4. Severe 5. Complete__________ inches

Building damage (due to wind)Contents/inventory damage
Machinery/equipment damage

1. None 2. Minor 3. Moderate 4. Severe 5. Complete
1. None 2. Minor 3. Moderate 4. Severe 5. Complete
1. None 2. Minor 3. Moderate 4. Severe 5. Complete

13. Did your business lose any of the following utilities? And for how long?
[* N/A: not applicable, if the business does not use this service, please indicate N/A; DK: don’t know]electric power?
water?
natural gas?

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have electricity

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have water

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have natural gas
A-19sewer?
landline phone?
cell phone service?
Internet/IT?

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have sewer

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have landline

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have cell service

1. Yes 2. No 3. DK 4. N/A_____Hours or _____ Days

still don’t have internet/IT

14. Did this business experience physical accessibility problems after Hurricane Florence like street or sidewalk
closures?

1. Yes 2. No

15. Immediately following Hurricane Florence, did you completely cease operation at this location?
1. Yes 2. No
16. How long did it take for your business to resume operations? ____________ (days)
17. Did you have any employee(s) who could not report to work due to any of the following issues after
Hurricane Florence:transportation problems?
1. Yes 2. No
their need to repair their home?
1. Yes 2. No
their children were not back to school?
1. Yes 2. No
disaster-related physical health issues?
1. Yes 2. No
disaster-related mental health issues?
1. Yes 2. No
18. Did this business experience loss of customers after Hurricane Florence?
1. Yes (_____% loss of customers) 2 . No
19. Where do you feel your business is in the process of recovery since Hurricane Florence?
1. Still in survival/response mode
2. Recovering
3. Mostly recovered
4. Fully recovered
5. Still in operation but will never recover (please explain) _______________________________
20. Compared to before Hurricane Florence, what is the % capacity at which you are NOW operating? _____%
21. a. Immediately before Hurricane Florence, how many full time and part time employees did thisbusiness have?
Before:Full time __________Part time ____________
b. What about now?
Now:

Full time __________Part time ____________

A-20

22. Please indicate your level of agreement with the following statement.
We have more suppliers outside our city 1. Strongly
than we did prior to Hurricane Florence
disagree

2. Disagree

4. Agree

3. Neutral

5. Strongly
agree

RECOVERY FINANCE“Thank you! These next few questions are about recovery finance options following Hurricane Florence.”
23. Did you have flood insurance coverage on the building, contents, or business interruption before
Hurricane Florence? Did you file claims and receive money?

Building insurance
Content insurance
Business interruption
insurance
Liability insurance

Had Insurance?

Filed Claim?

Received Money?

1. Yes
1. Yes
1. Yes

2. No
2. No
2. No

1. Yes 2. No
1. Yes 2. No
1. Yes 2. No

1. Yes
1. Yes
1. Yes

2. No
2. No
2. No

1. Yes

2. No

1. Yes 2. No

1. Yes

2. No

Received When?

(months after Florence)

24. Did you receive any of the following assistance in recovery from Hurricane Florence?
Assistance Description

Applied?

Received?

a. FEMA financial assistance

1. Yes 2. No

1. Yes 2. No

b. SBA (Small Business Administration) loan

1. Yes 2. No

1. Yes 2. No

c. Other federal or state funds (specify):______________________________________
d. Local government funds (specify):______________________________________
e. Financial assistance from any church or othernon-government organizations?
f. Clean up or repair help from church or othernon-government organizations?
g. Loan from a Bridge Loan** program

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

1. Yes 2. No

h. Private/bank loans

1. Yes 2. No

1. Yes 2. No

i. Crowdsourcing (e.g. online)

1. Yes 2. No

1. Yes 2. No

j. Fundraisers (e.g. in-person/events/not online)?

1. Yes 2. No

1. Yes 2. No

k. Other(s)? _____________________

1. Yes 2. No

1. Yes 2. No

Received When?

(months after Florence)** Bridge Loan: typically, loans between $1,000 and $50,000 for up to one year. While the bridge loan is asource of expedient funds, it is not designed to be the primary source of assistance for affected small businesses
25. How long can this business function in a deficit [enter number of days, weeks, months, and specify whichtime unit]? ________________A-21

MITIGATION, PREPAREDNESS, AND ADAPTATION“These next questions are going to capture information about mitigation, preparedness, and adaptation
following Hurricanes Matthew and Florence.”
26. IF YOUR BUSINESS OWNED/OWNS THE BUILDING. Did you adopt any of the following mitigation
strategies? [Please indicate with an “X” all that apply; If the business does not own the building, skip to Q27]

Mitigation or
Preparedness ActionNo
adoption

AdoptedbeforeHurricane
Matthewa. Floodproofing of building done byprofessionals
b. Floodproofing of building done by
nonprofessionals (including yourself)c. Secured a secondary storage locationd. Had the building structurally assessed byan engineer

Adoptedbetween
AdoptedHurricane
after
Matthew and Hurricane
Florence
Hurricane
Florence

27. FOR ALL BUSINESSES: Did this business have any of the following emergency management strategies inplace before Hurricane Florence? Does it still have or has it added them since Hurricane Florence? [Pleaseindicate with an “X” all that apply]
Mitigation or
Preparedness ActionNo
adoption

AdoptedbeforeHurricane
Matthewa. Performed risk assessment to identify
business vulnerability todisastersb. Adopted strategies to stay informed ofweather watches andwarnings
c. Developed a written emergency action
plan or checklist
d. Assigned disaster responsibilities tospecific employees
e. Performed emergency management drills
regularly

Adoptedbetween
AdoptedHurricane
after
Matthew and Hurricane
Florence
Hurricane
Florence

BUSINESS INFORMATION“Now, I’d like to ask you general questions about your business and yourself.”
28. In what year was this business established at this location? ______________ (Year)

A-22

29. Does this business own or rent the building?
1. Own (including buying the building with mortgage)
2. Rent
3. Other (please specify) __________________________________
30. What was the business ownership structure before Hurricane Florence?
1. Single owner
2. Partnership (multiple owners)
3. Corporation or franchise
4. Cooperative
Other (please specify): __________________________________OWNER/MANAGER DEMOGRAPHICS
31. How many years have you worked as a business owner/manager here or for other businesses?
_____________ (years)
32. What is your age? ________________ (years)
33. What is your number of years of schooling? Enter number of years _______ and indicate
type of diploma or degree: 1. High School 2. Associate degree

3. Bachelors

4. Masters or higher degree

34. Are you Hispanic? 1. Yes 2. No
35. What is your race? Please select one or more.
1) White
2) Black or African American3) American Indian or Native American4) Asian

5) Native Hawaiian or other Pacific Islander
6) [If respondent selects >1, surveyor must specify
codes] _______________

We would like to get some additional information from you in order to make future surveys easier and at yourconvenience. Would you be willing to provide your first name and email address? YOU WILL NOT RECEIVEANY EMAILS BEYOND OUR REQUEST FOR ADDITIONAL CLARIFICATION ON THIS SURVEY.
Name:______________________________
Business Email:________________________________

Lastly, if you have any comments about the survey and/or business recovery after the flood, please write
them down in the space below.

A-23

THANK YOU VERY MUCH FOR COMPLETING THE SURVEY!
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the Office ofManagement and Budget (OMB). Notwithstanding any other provisions of the law, no person is required torespond to, nor shall any person be subject to a penalty for failure to comply with, a collection of information
subject to the requirements of the PRA unless that collection of information displays a currently valid OMB
control number. For this collection, the OMB Control number is:0693-0078 with an expiration date: July 31,
2019. Public reporting burden for this collection is estimated to be 15 minutes per survey, including the time forreviewing instructions, searching existing data sources, gathering and maintaining the data needed andcompleting and reviewing the collection of information. Send comments regarding this burden estimate or any
aspect of this collection of information, including suggestions for reducing this burden, to the National Instituteof Standards and Technology, Attn: Dr. Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD
20899-1710, telephone 301-975-6133, or via email:jennifer.helgeson@nist.gov.

Privacy Act Statement
Authority: The Paperwork Reduction Act of 1980 (Pub. L. No. 96-511, 94 Stat. 2812, codified at 44 U.S.C. §§3501–3521).
Purpose: The National Institute for Standards and Technology (NIST) conducts Community Resilience researchand surveys through the Generic Paperwork Reduction Act Clearance, OMB CONTROL NO. 0693-0078
Expiration date: 07/31/2019.
Routine Uses: NIST will use this information to conduct a systematic process evaluation of the NIST Community
Resilience Planning Guide implementation. This is not a Privacy Act system of Records, therefore there is no
System of Records Notice associated with this collection.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating yourvoluntary consent for NIST to use of the information you submit for the purpose stated.

A-24

Appendix 4B - Wave 3c Business Information sheet
Hurricane Matthew Community Recovery Study for Lumberton, NC
Center for Risk-Based Community Resilience Planning
A U.S. National Institute of Standards and Technology-funded Center of Excellence_______________________________________________________________________The Center for Risk-Based Community Resilience Planning is based at Colorado State University
in Fort Collins, Colorado, and includes collaborations with researchers from universities acrossthe United States. The National Institute of Standards and Technology’s Community Resilience
Program is based in Gaithersburg, Maryland and includes engineers, economists, andsociologists. Collectively, we are working to understand what makes a community “resilient” –or able to bounce back – in the face of disaster.
Our research in Lumberton focuses on community recovery following the flooding that occurred
due to Hurricanes Matthew and Florence. We are returning to Lumberton for our third year tofollow up on progress with recovery. We will be collecting information from businesses likeyours to gain knowledge on the impacts and the recovery from Hurricane Matthews andFlorence. We hope to learn from your experiences to help communities better prepare forsimilar events in the future.
This research is part of a five-year project that will be carried out by experts from engineering,
the social sciences, economics, and many other disciplines. This community was selected as oneof our five research locations around the country that we hope to learn from. Our field team will
be in Lumberton from April 11 through 21, 2019.
Because NIST is part of the federal government, this research was reviewed through a special
process. This collection of information contains Paperwork Reduction Act (PRA) requirements
approved by the Office of Management and Budget (OMB). Notwithstanding any otherprovisions of the law, no person is required to respond to, nor shall any person be subject to apenalty for failure to comply with, a collection of information subject to the requirements of the
PRA unless that collection of information displays a currently valid OMB control number. For thiscollection, the OMB Control number is:0693-0078 with an expiration date of July 31, 2019.
Public reporting burden for this collection is estimated to be 15 minutes per survey, including
the time for reviewing instructions, searching existing data sources, gathering and maintaining
the data needed and completing and reviewing the collection of information. Send comments
regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology,
Attn: Dr. Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710,
telephone 301-975-6133, or via email:jennifer.helgeson@nist.gov
If you have more general questions about the project or the Center for Risk-Based Community
Resilience Planning, please contact: Dr. John van de Lindt at 970-218-4076 or via email:jwv@engr.colostate.edu.

A-25

Appendix 4C - Wave 3c Business Consent Script
Two-Year Post-Hurricane Matthew Field Study in Lumberton, North Carolina

Business Recovery Survey
Consent Script

OMB CONTROL NO. 0693-0078 Expiration date: 07/31/2019
Hello, my name is (interviewer name) and I am a researcher from (name of university or National
Institute of Standards and Technology) in the (department name/Engineering Laboratory). We areconducting a research study on recovery following the flooding that occurred in Lumberton, N.C. from
Hurricane Matthew and Hurricane Florence. We would like to speak with you about how this event
affected your business. In particular, we are interested in learning about the process of recovering fromthe flooding.
This study is part of a larger project led by Center of Excellence for Risk-Based Community Resilience
Planning at Colorado State University. This project is led by Drs. John van de Lindt and Bruce
Ellingwood, both Professors from the Civil and Environmental Engineering Department and is funded by
the National Institute of Standards and Technology (NIST).
We would like to ask you some brief survey questions about your business’s experience after the flood
as well as some details about your business during this time. Participation will take approximately
fifteen minutes. Your participation is voluntary. If you decide to participate in the study, you may
withdraw your consent and stop participation at any time without penalty.
We will be collecting information about the damage to your business, the repair process, and how theflood disrupted your business’s employees, supply chain, and revenues. When we report and share ourfindings, we will combine the data from all participants into summary statistics and tables so no unique
individual or business can be identified. There are NO KNOWN RISKS or direct benefits to you. We hopeto gain more knowledge on how you and others were affected by Hurricane Matthew and the flooding,so that we can learn from your experiences to help communities better prepare for similar events in the
future.
So again, we would like to speak with an owner or manager of the business. Would that person be you
and would you be willing to participate?
This collection of information contains Paperwork Reduction Act (PRA) requirements approved by the
Office of Management and Budget (OMB). Notwithstanding any other provisions of the law, no person isrequired to respond to, nor shall any person be subject to a penalty for failure to comply with, acollection of information subject to the requirements of the PRA unless that collection of information
displays a currently valid OMB control number. For this collection, the OMB Control number is:0693-0078
with an expiration date: July 31, 2019. Public reporting burden for this collection is estimated to be 15
minutes per survey, including the time for reviewing instructions, searching existing data sources,
gathering and maintaining the data needed and completing and reviewing the collection of information.
Send comments regarding this burden estimate or any aspect of this collection of information, including
suggestions for reducing this burden, to the National Institute of Standards and Technology, Attn: Dr.
Jennifer Helgeson, NIST, 100 Bureau Drive, MS 8603, Gaithersburg, MD 20899-1710, telephone 301-9756133, or via email:jennifer.helgeson@nist.gov.

A-26